Image quality control in image forming apparatus

Abstract: When a status transition occurs, case data are collected and a new control rule is generated based on those case data. A control rule memory can store a plurality of control rules. A plurality of control rules are selectively read out from the control rule memory, and combined in accordance with their adaptabilities into an application control rule. An error between a target value and a measured control quantity is applied to the application control rule, to thereby calculate a correction value of an operation quantity. An operation part of an image forming apparatus main body is controlled based on the correction value so that the control quantity becomes the target value.

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image forming apparatuses of various types such as an electrophotographic type and an ink jet type, and a control method therefor. In particular, the invention is intended to allow control for keeping the image quality at a given level to be carried out at a low cost with high accuracy, as well as to greatly reduce man-hours necessary for data collection and design optimization in developing a new product. 2. Description of the Related Art Conventionally, in an image forming apparatus of an electrophotographic type, feedback control is employed to keep the image density at a optimized level. This is because in the electrophotographic type apparatus using static electricity, due to variations in the environmental conditions such as temperature, humidity, and the like, or due to the aging degradation of a photoreceptor or a developing agent, the image output state of the apparatus per se varies, which in turn varies the density reproduction performance. In a general feedback control method, a density reproduction state is monitored by a density patch to find a difference between a target density and the thus monitored density, and the resultant difference is multiplied by a feedback gain to thereby calculate a correction quantity of a set value of a controlling actuator. Here, in most cases, a developed patch is used as the above density patch. The reason for this is that the developed image can be formed and erased easily when compared with a transferred image or a fixed image to be formed on paper, and also that the developed image patch is extremely highly correlated with a fixed image density which can be obtained by a user. Also, as the controlling actuator, there are often used a charger application voltage, a quantity of exposure light, and a developing bias which have a decisive influence on the developing characteristics of the image forming apparatus. For example, in feedback control techniques disclosed in Japanese Unexamined Patent Publication Nos. Sho. 63-177176, Sho. 63-177177, and Sho. 63-177178, the developed image density is controlled to a desired value by varying a developing potential. In this developing potential varying technique, both of one-component and two-component developing processes can be used. However, the optimum developing potential is constantly influenced by uncontrollable various external factors such as temperature, humidity, the cumulative number of prints formed and the like and, therefore, when the charging potential, the quantity of exposure light and the developing bias are set, these external factors must be always taken into account. Also, a relationship between a quantity showing the status of an image forming apparatus main body such as temperature, humidity and the like (which is hereinafter referred to as a status quantity) and the set values of the charging potential, quantity of exposure light and developing bias is complicated and, at the present stage of technology, a satisfactory physical model has not been established on this relationship. In view of the above circumstances, for example, control is employed which is quantified by using approximate equations. However, in the electrophotographic technology which consists mainly of an electrostatic process, normally, the optimum set values of the charging potential, the quantity of exposure light and bias have a non-linear relationship with respect to the status quantities, so that a satisfactory control accuracy cannot be obtained. Under these conditions, it is necessary to previously check various environmental conditions such as ambient effects under a high temperature and low humidity state or under a low temperature and low humidity state, effects due to degradation with time, and the like. That is, as the target control performance is higher, it is necessary to collect more detailed data in a wider condition range, which in turn requires vast man-hours for development. Also, even the feedback gain that is determined at the cost of vast man-hours cannot be always recognized as the optimum gain partly because the individual image forming apparatuses differ from one another and partly because users' machine using conditions vary widely. Especially, the effects of the aging-degraded image forming apparatus on the image density vary greatly depending on the degrees of degradation of parts used in the respective apparatus and the conditions under which the apparatus are used. Therefore, the image forming apparatus produced using the above density control technology cannot always provide a satisfactory image density control performance on the market when it is used for a long period. Further, in order to obtain a desired control accuracy, the above-mentioned control method, in most cases, requires a potential sensor for monitoring the charging potential and exposure potential as intermediate parameters, as well as a temperature sensor and a humidity sensor respectively for detecting environmental conditions, which results in the increased manufacturing cost. Recently, as disclosed in Japanese Unexamined Patent Publication Nos. Hei. 4-319971 and Hei. 4-320278 (US 5,216,463), there have been employed a fuzzy control method and a neural-network-based control method. These control methods are able to cope with even a non-linear case in which a relationship between the input and output is complicated. That is, these two control methods are used solely as means for enhancing the control accuracy. Therefore, these methods are little helpful in solving the above-mentioned problems: the vast man-hours necessary to gather a large amount of data; the increased cost caused by use of the sensors; and the failure to secure the long-term satisfactory image density control performance of the individual image forming apparatus after they are introduced into the market. When the control accuracy of the image forming apparatus is enhanced by using the fuzzy or neural-network-based control method, in order to make the most use of its advantage of being suitable for a multi-input/multi-output operation, in most cases, there are employed a large number of inputs, that is, there are used a large number of sensors, which instead leads to an increase in the cost of the image forming apparatus used. Further, in the fuzzy control method, membership functions must be tuned by engineers and, in the neural-network-based control system, although a learning operation itself can be automated, teacher data for the learning operation must be previously prepared by the engineers. That is, both of them require considerably man-hours for development. In addition, even when the aging degradation data are previously collected and the fuzzy or neural-network-based control method is used with the thus collected data taken into account, if the input/output relationship itself has varied due to the actual aging degradation of parts used, difference between apparatuses, and replacement of parts, the image forming apparatuses based on such method are not able to cope with such varied input/output relationship by themselves. That is, the image density control performances of the individual image forming apparatuses after they are introduced into the market cannot be assured for a long period even when they employ the fuzzy or neural-network-based control method. SUMMARY OF THE INVENTION The present invention aims at eliminating the above-mentioned problems found in the conventional image forming techniques. Accordingly, it is an object of the invention to provide an image forming technique which is capable of controlling an operation amount simply and accurately with no need for engineers to grasp various environmental conditions previously, thereby being able to reduce man-hours for development to a great extent. It is another object of the invention to provide a technique which, even when a vast number of image forming apparatuses are introduced into the market and they are respectively used in various ways and the parts thereof are replaced as the occasion demands, can maintain the quality of images formed by the individual image forming apparatuses at a high level. To attain the above object, according to the invention, there is provided an image forming apparatus and a method of controlling the same as defined in claims 1 and 25. Preferred embodiments are defined in the dependent claims. Fig. 1 is a block diagram of a first embodiment which corresponds to the principle operation of the present invention; Fig. 1A is a block diagram showing a modification of the first embodiment; Fig. 2 is a block diagram showing the structure of an image output part IOT included in a second embodiment in which the invention is applied to an image forming apparatus of an electrophotographic type; Fig. 3 shows how to generate patches for density detection in the second embodiment; Fig. 4 shows timings for forming the patches and an input signal image in the second embodiment; Fig. 5 is a graph showing the density of an image formed in the second embodiment; Fig. 6 is a block diagram showing the configuration of a control part 20 in the second embodiment; Fig. 7 is a table of case data to be stored in a control case memory 25 shown in Fig. 6; Fig. 8 is a table of control rules to be stored in a control rule memory 29 shown in Fig. 6; Fig. 9 is a flowchart showing an initializing operation of the second embodiment; Figs. 10 and 11 illustrates the initializing operation of Fig. 9; Fig. 12 is a flowchart showing the basic operation of the second embodiment which is performed after the initialization; Fig. 13 is a flowchart showing a process for forming a main image in the basic operation of Fig. 12; Fig. 14 is a flowchart showing a process for forming a main image and a patch image in the basic operation of Fig. 12; Fig. 15 is a flowchart of a process for adapting an application control rule in the basic operation of Fig. 12; Fig. 16 illustrates the operation of Fig. 15; Figs. 17 and 18 are a flowchart showing a more detailed version of the process of Fig. 15; Fig. 19 is a flowchart showing details of a case data adding process in the process of Fig. 17; Figs. 20 and 21 are a flowchart showing details of a control rule forming/correcting process in the process of Fig. 18; Fig. 22 is a flowchart showing details of a process for calculating coefficients of a control rule in the process of Fig. 20; Fig. 23 shows an example in which a polyhedron is used as a control rule; Fig. 24 shows an example in which a curved surface is used as a control rule; Fig. 25 is a table showing how to control rewriting of a control rule using cumulative adaptabilities; Fig. 26 is a block diagram showing the configuration of a control part in third and fourth embodiments of the invention; Fig. 27 is a schematic block diagram showing comparison with an allowable range according to the second embodiment; Fig. 28 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the second embodiment; Fig. 29 is a schematic block diagram showing comparison with an allowable range according to the third embodiment; Fig. 30 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the third embodiment; Fig. 31 is a conceptual diagram showing how to obtain a control case according to the second embodiment; Fig. 32 is a conceptual diagram showing how to obtain a control case according to the third embodiment; Fig. 33 is a schematic block diagram showing comparison with an allowable range according to the second embodiment; Fig. 34 is a conceptual view showing how to obtain a control case and generate a cluster; Fig. 35 is a schematic block diagram showing comparison with an allowable range when the number of prints and the working time are used as status quantities; Fig. 36 is a conceptual diagram showing how to obtain a control case and generate a cluster when the number of prints and the working time are used as status quantities; Fig. 37 is a schematic diagram showing comparison with an allowable range according to the fourth embodiment of the invention; Fig. 38 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the fourth embodiment; Fig. 39 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the second embodiment; Fig. 40 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the fourth embodiment; Fig. 41 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the second embodiment; Fig. 42 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the fourth embodiment; Fig. 43 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the second embodiment; and Fig. 44 is a conceptual diagram showing how to obtain a control case and generate a cluster according to the fourth embodiment. DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiment 1 First, a description will be given of a first embodiment which is directed to the basic constitution according to the invention. Now, Fig. 1 is a block diagram showing the whole configuration of this embodiment. In Fig. 1, an image forming apparatus main body 100 is an image forming apparatus which uses an electrophotograhic system, an ink jet recording system or the like. In the image forming apparatus main body 100, there is packaged a function block (not shown) which is used to carry out an image forming process. The operation part 101 of the image forming apparatus main body 100 is an operation part which is used to output a control quantity regulating the quality of an image formed, for example, an operation quantity determining the density of the image. The operation part 101, in the electrophotographic system, comprises the output power of a laser radiation device for forming a latent image, the charged potential of a charger, the charged quantity of a toner, and the like. The control quantity measuring part 102 of the image forming apparatus main body 100 is a measuring device which is used to measure the control quantity. For example, if the control quantity is the image density, then the measuring part 102 is an optical sensor which measures the image density. A control part 103, together with the image forming apparatus main body 100, constitutes the image forming apparatus. The control part 103 is normally mounted in the image forming apparatus main body 100, but it can also be provided separately from the image forming apparatus main body 100. The control part 103 controls the operation quantity of the operation part 101 so that the control quantity of a given image to be formed by the image forming apparatus main body 100 can be maintained at a target value. The control rule memory part 105 of the control part 103 is used to store one control rule or a plurality of control rules. The control rule is a function which defines a relationship between the operation quantity and the control quantity. This function is represented by a surface (a plane or a curved surface) in a coordinate space which is defined by the operation and control quantities. One control rule is generated by using two or more pieces of coordinate data, for example, three pieces of coordinate data that are represented by the operation quantity that is actually applied to the operation part 101 of the image forming apparatus main body 101 and the control quantity that is measured by the control quantity measuring part 102 when the operation quantity is actually applied. The control rule stored in the control rule memory part 105 is supplied to a control rule deviation computing part 106, in which it is used to compute the adaptabilities of the respective control rules as will be described later. The control rule is also supplied to a rule select part 107, while part or all of the control rules are supplied to an application control rule calculation part 108. The control rule deviation computing part 106, for each of the control rules, finds a deviation or a difference between a control quantity expected when the control rule is applied to an operation quantity in a given image forming process (for example, the last or previous image forming process to be measured) and an actual control quantity in the given image forming process. The difference is supplied to the application control rule calculation part 108. Responsive to this, the calculation part 108 combines together the control rules using a weight which increases as the difference decreases, and outputs the resultant combination as an application control rule which can be applied to determine an operation quantity. The combination of the control rules can be achieved, for example, by adding together functions representative of the control rules with weighting. The parts 105-108 constitute an application control rule part 115. The rule selecting part 107 selects the control rule in accordance with the deviation or difference supplied from the control rule deviation computing part 106. For example, the rule selecting part 107 selects a given number of control rules in increasing order of the size of the deviations, or selects the control rules the deviations of which are equal to or less than a preset value. An operation quantity correcting part 109 varies the operation quantity of the operation part 101 in accordance with the application control rule that is output by the application rule calculation part 108. That is, an error between a target value set by an target value setting part 110 and a control quantity measured by the control quantity measuring part 102 is detected by an error detector 111, and an operation quantity (or a variation thereof) to cancel this error is computed according to the application control rule. The thus computed operation quantity is then added to the operation part 101. A status transition judging part 112 is used to judge the transition of the status of the image forming apparatus main body. That is, the term "status" means such status of the image forming apparatus main body 100 that influences the quality of an image, and the status includes temperature, humidity and the like. The transition of the status may be judged in accordance with the temperature, humidity and the like. However, more conveniently, the transition of the status can be judged from an elapsed time after the previous status transition, or from the number of prints of images formed after the previous status transition. If the status is changed, then it may be difficult that the new status can be dealt with accurately only by the control rule used until then. For this reason, a control rule generating part 113 generates a new control rule which can be used to cope with the changed or new status. This new control rule is also generated in accordance with the operation quantity in the image forming process and the measured control quantity. The thus newly generated control rule is stored in the control rule memory part 105 by a memory control part 114. When the quantity of the control rules to be stored reaches a given quantity, a new control rule is registered instead of the oldest control rule, that is, the control rule that has not been used for the longest time. The control rule can also be stored and managed by other various means. In the above configuration, if the transition of the status is judged in accordance with to the elapsed time or the number of image prints formed, then there is generated a control rule which is properly representative of the current status and the control of the image forming apparatus main body 100 is executed according to an application control rule reflecting the current status. Also, the adaptabilities of the respective control rules are judged according to the deviations of the control rules, and a control rule having a higher adaptability to the current status is reflected on the application control rule with a heavier weight. This makes it possible to realize a suitable control for the status transition and thus a fine control can be achieved by use of a reduced number of control rules. Fig. 1A shows a modification of the first embodiment of Fig. 1. In the modification, an application control rule is generated by combining control rules that have been prepared in advance. An output signal of the control quantity measuring part 102 is sent to the control rule deviation control part 106, which, as described later, computes a deviation to be used for selection among control rules that have been prepared in the control rule memory part 105 based on the output signal of the control quantity measuring part 102. Embodiment 2 Next, a description will be given below of a second embodiment which is a more detailed configuration according to the invention. In this embodiment, the present invention is applied to an image forming apparatus of an electrophotographic type in which, after the surface of a light conductor is charged uniformly with a scorotron charger, latent images are formed by radiation of laser beams and the thus formed latent images are then developed with a toner. In this embodiment, a control quantity is an image density (which includes two kinds of densities, that is, a solid density and a highlight density), while an operation quantity is the grid potential of the scorotron charger and the output of the output part of the laser beams. (1) Configuration of Image Forming Apparatus Fig. 2 shows the outline of the image output part IOT (Image Output Terminal) of an image forming apparatus according to the present embodiment. In Fig. 2, there are omitted an image read part and an image processing part. That is, there is shown only the image output part IOT of an electrophotographic type. Describing an image forming procedure with reference to Fig. 2, an original image signal, which is obtained by an image read part (not shown) reading a document or formed by an external computer (not shown), is firstly processed properly in an image processing part (not shown) to thereby obtain an input image signal. The thus obtained input image signal is input into a laser output part 1, where it is used to modulate a laser beam R. Then, the laser beam R that is modulated by the input image signal is raster radiated onto a photoreceptor 2. On the other hand, the photoreceptor 2 is charged uniformly by a scorotron charger 3 and, if the laser beam R is radiated onto the photoreceptor 2, then there is formed on the surface of the photoreceptor 2 an electrostatic latent image which corresponds to the input image signal. Next, the electrostatic latent image is toner developed by a developing device 6, the developed toner is transferred to a sheet (not shown) by a transfer device 7, and the developed toner is fixed by a fixing device 8. After then, the photoreceptor 2 is cleaned by a cleaner 11, which completes one image forming operation. Also, reference character 10 designates a developed density sensor which detects the densities of a developed image patch (to be discussed later) formed outside an image area. (2) Developed Patch Forming Mechanism and its Monitoring Mechanism Here, a description will be given below of a developed image patch and its monitoring mechanism employed in the present embodiment. The developed image patch is used to monitor an output image density and, as shown in Fig. 3, as a density patch, there are employed two kinds of density patches, that is, a solid (dot coverage of 100%) density patch PA1 and a highlight (dot coverage of 20%) density patch PA2. As shown in Fig. 3, either of the solid density patch PA1 or highlight density patch PA2 is set to be of the order of 2 to 3 cm square and is formed along the center line L1 outside the image area of the photoreceptor 2. That is, as shown in Fig. 4, after a latent image is formed in the image area 2a, the solid density patch PA1 and highlight density patch PA2 are formed sequentially in a vacant area 2b. Also, the density sensor 10 comprises an LED radiation part for radiating light onto the surface of the photoreceptor 2, and a photosensor for receiving a regularly reflected light or a diffused light from the surface of the photoreceptor 2, while a line L1 shown in Fig. 3 is the detect line of the developed image density sensor 10. That is, the solid density patch PA1 and highlight density patch PA2 are formed on the detect line L1 and pass sequentially in the neighborhood of the developed image density sensor 10. Here, Fig. 5 is a graphical representation which shows an example of the output signals of the developed image density sensor 10. As shown in Fig. 5, there is firstly obtained a density detection signal corresponding to the image of a document and, after then, there are obtained the density detection signals of the solid density patch PA 1 and highlight density patch PA2 respectively. Since the solid and highlight density patches PA1 and PA2 are formed outside the image area, they are not transferred to the sheet but are erased when they pass through the cleaner 11 portion. The reason why the densities of the developed image patches are detected in the present embodiment is that they have a high correlation with the density of a fixed image that is given to a user (namely, a final image density) and also that the patches can be removed by the cleaner 11. Also, in other timings than the image forming timing, the developed image patches may also be formed within the image area. Further, as the developed image patch, there can also be used a developed image patch of other dot coverage. (3) Configuration of Control Part Next, Fig. 6 is a block diagram showing the configuration of a control part 20 which controls the scorotron charger 3 and the laser output part 1. In Fig. 6, reference character 21 designates a density adjusting dial which can be operated by an operator to set a value corresponding to a desired density. The set value of the density adjusting dial 21 is converted by a converter 22 to a value corresponding to each of the outputs of the developed image density sensor 10 (in the present embodiment, a value in the range of 0 - 255). A target density output from the converter 22 is stored in a control quantity memory 23. In this case, the control quantity memory 23 stores therein an allowable error as well. On the other hand, the output signal of the developed image density sensor 10 is compared with the output signal of the memory 23 in a density comparator 24. This comparison is made with reference to the allowable error stored in the memory 23. The output signal of the developed image density sensor 10 is supplied to a control rule retrieval unit 30 when a difference between the above output signals is within an allowable value, while it is supplied to a control case memory 25 when the difference is equal to or larger than the allowable value. The control case memory 25 is a memory which stores control cases and, in particular, it stores three kinds of quantities in a set, that is, a status quantity (a representative value), an operation quantity, and a control quantity. The reason why the control cases are stored in this manner is to carry out various kinds of controls in accordance with the control cases stored in the past. Here, the term "status quantity" to be stored in the control case memory 25 may be the temperature and humidity that have dominant influences on the electrophotographic process, or a quantity of degradation with the passage of time. Since these quantities can be considered almost constant within a limited period of time, in the present embodiment, instead of such quantities, there is used the time of occurrence of a case (which includes the date, hour, minute, and second), or the number of image prints formed. If the case occurrence time is within a given time unit (that is, a previously determined time unit such as 3 minutes, 5 minutes, 10 minutes or the like), then the status of the image output part IOT is considered constant during the given time unit. This is because it can be expected that, if two cases occur at mutually near times, then the two cases are almost under the same temperature and humidity condition and the degrees of degradation with the passage of time thereof are also almost the same. Also, the time data that represent the occurrence times, in the present embodiment, are supplied from a clock timer 40 shown in Fig. 6. Also, it can be checked from the cumulative number of prints formed whether the two cases are under the same condition or not. Next, the operation quantity means the adjustment quantities of parameters which are used to vary the output values of devices to be controlled and, in the present embodiment, the operation quantity includes two kinds of quantities, that is, the grid voltage set value (0 - 255, which is hereinafter abbreviated to a scoro set value) of the scorotron charger 3 and the laser power set value (0 - 255, which is hereinafter abbreviated to an LP set value). The reason why these two quantities are used as the operation quantity is that the final image density to be controlled includes the solid density portion and highlight density portion and also that the scoro set value and LP set value have a high correlation with the solid density and highlight density. Also, the scoro set value and LP set value are respectively stored in an operation memory 32, while the scoro and LP set values that correspond to the output signal of an operation quantity correction value calculating unit 31 are read out from the operation quantity memory 32 as the occasion demands. The scoro set value read out from the operation quantity 32 is supplied to a grid power source 15 and, responsive to this, the grid power source 15 applies to the scorotron charger 3 a voltage corresponding to the scoro set value. Also, the LP set value read out from the operation quantity memory 32 is supplied to a light quantity controller 16 and, responsive to this, the light quantity controller 16 applies to laser output part 1 laser power corresponding to the LP set value. Next, the control quantity to be supplied to the control case memory 25 is the output signal of the developed image density sensor 10 and, as a result of the above operation, in the control case memory 25, there are stored such control cases as shown in a table in Fig. 7. In this table, for example, referring to a case 1 in a cluster 1, the status quantity (occurrence time) is 951201120010 ('95, December 1, 12:00:10); the LP set value is 83; the scoro set value is 130; and, the control quantity (sensor output value) is 185 in the solid portion and 23 in the highlight portion. Also, in a case 1 in a cluster 2, the status quantity is 951202090005 ('95, December 2, 09:00:05); the LP set value is 148; the scoro set value is 115; and, the control quantity is 185 in the solid portion and 30 in the highlight portion. As will be discussed later, a control rule is formed for each cluster of control cases. Next, a status quantity comparator 26, a cluster memory 27, and a control rule calculating unit 28 shown in Fig. 6 have a function of referring to the control cases stored in the control case memory 25 and extracting control rules therefrom. The operations of these blocks will be described later in detail. A control rule memory 29 is a memory which stores a plurality of control rules operated by the control rule calculating unit 28. When the control rule memory 29 receives a request from a control rule retrieval unit 30, the control rule memory 29 gives the control rule retrieval unit 30 a control rule corresponding to the request. In this case, the control rule retrieval unit 30 requests the control rule memory 29 to supply a control rule which corresponds to a density difference supplied from the density comparator 24 and an operation quantity (that is, LP set value and scoro set value) supplied from an operation quantity memory 32. The control rule memory 29, as shown in Fig. 8, stores the coefficients (gains) of control rules. The previously described control case memory 25 stores the cases necessary to form control rules, but, basically, no other control rules than the newest control rule are not used. For this reason, each time a cluster for cases is formed, the data on the previous cluster are reset. Next, the operation quantity correction value calculating unit 31 finds the correction value of the operation quantity using a control rule retrieved by the control rule retrieval unit 30 and then supplies the resultant correction value to the operation quantity memory 32. To clearly express a control rule which is applied to compute the correction value of the operation quantity (the operation quantity itself can also be used), this control rule is referred to as an application control rule. Now, on receiving the operation quantity correction value, the operation quantity memory 32 supplies an operation quantity corresponding to the operation quantity correction value, that is, the LP set value and scoro set value to the grid power source 15 and light quantity controller 16 respectively. On the other hand, a reference patch signal generator 42 is a circuit which directs the formation of the solid density patch PA1 and highlight density patch PA2 and, at a patch forming timing, outputs a proofreading reference patch signal to the image forming part IOT. As a result of this, the solid density patch PA1 and highlight density patch PA2 shown in Fig. 3 are formed. In this case, the operating timing of the reference patch signal generator 42 is instructed by an I/O adjusting part 41. The I/O adjusting part 41 monitors a time signal output from a clock timer 40 and supplies an operating timing signal to the reference patch signal generator 42 so that the solid density patch PA1 and highlight density patch PA2 can be respectively formed at given positions. (4) Initializing Operation Next, a description will be given below of the operation of the present embodiment having the above-mentioned configuration. First, an initialization processing (a so-called function setup processing) will be described mainly with reference to Fig. 9. At a first step, an engineer sets properly the scoro set value and LP set value respectively selected as the controlling parameters (S11). The control part 20 forms the solid density patch PA1 and highlight density patch PA2 (S12), the optical densities of the two patches are respectively measured by the developed image density sensor 10 (S13), and the measured contents are stored in the control case memory 25 as a control case (S14). As a result of this, the first control case (control case 1) is stored in the control case memory 25. Similarly, while the engineer is varying the scoro set value and LP set value, the engineer allows two more control cases to be stored into the control case memory 25. That is, during the control unit setup time (within the time in which the status quantity is constant), the engineer forms a total of three control cases and stores them into the control case memory 25 (S15). When the three control cases in the initializing operation are stored in the control case memory 25, then the storage contents of the memory 25 are supplied through the status quantity comparator 26 and cluster memory 27 to the control rule calculating unit 28, where a control rule is generated. The thus generated control rule is extracted as control case planes as shown in Fig. 10 (S16). To determine the control case planes of Fig. 10, there are necessary three independent control cases. Of course, four or more control cases may also be used. In this case, a square mean error method or the like is used to determine the optimum control case planes. In Fig. 10, P1, P2 and P3 respectively designate three points which point out three combinations of the scoro set values and LP set values in connection with the three control cases in the initializing operation. Here, points respectively indicating the highlight densities (that is, the detected densities of the highlight density patches) corresponding to the points P1, P2 and P3 are expressed as H1, H2 and H3, respectively. Similarly, points respectively representing the solid densities (the detected densities of the solid density patches) corresponding to the points P1, P2 and P3 are expressed as B1, B2 and B3, respectively. A plane passing through the three points B1, B2 and B3 is called a solid case plane BP, while a plane passing through the three points H1, H2 and H3 is called a highlight case plane HP. Here, when the status quantity remains unchanged, points pointing out solid densities obtained when the scoro set value and LP set value are varied properly are all contained in the solid case plane BP. Similarly, when the status quantity remains unchanged, points indicating highlight densities obtained when the scoro set value and LP set value are varied properly are all contained in the highlight case plane HP. In this manner, the solid case plane BP and highlight case plane HP indicate all cases obtained when the status quantity remains unchanged. In other words, these two planes represent the control rules that relate to the solid and highlight densities in the initializing operation. These processes complete the initializing process of the present embodiment. If the thus obtained control rules are used, then a scoro set value and an LP set value with respect to a given target density can be determined uniquely. That is, if a desired density indication value from a user is input, then a solid density (a solid target density) and a highlight density (a highlight target density) are calculated in accordance with the indication value input. As shown in Fig. 11, a plane containing the solid target densities (a solid target density plane BTP) and a plane containing the highlight target densities (a highlight target density plane HTP) are respectively superposed on the solid case plane BP and highlight case plane HP. An intersection line BTL intersecting the solid case plane BP and solid target plane BTP is the aggregation of points which satisfy the control rule on the solid density and indicate the solid target densities. Also, an intersection line HTL intersecting the highlight case plane HP and highlight target density plane HTP is the aggregation of points which satisfy the control rule on the highlight and indicate the highlight target densities. There is obtained a set of scoro and LP set values which satisfies both of the two intersection lines BTL and HTL. This set of scoro and LP set values is an intersection point between the projections of the intersection lines BTL and HTL in a plane which is defined by the coordinate axis of the scoro set values and the coordinate axis of the LP set values. The above can be expressed as follows by use of mathematical equations. The control rule on the solid density and the control rule on the highlight density can be expressed in the following manner: D100 = a1•LP + a2•SC + a3 D20 = b1•LP + b2•SC + b3 where D100 is a solid density, D20 is a highlight density, LP is an LP set value, and SC is a scoro set value while a1, a2, a3, b1, b2 and b3 are coefficients. If these expressions are solved with respect to the scoro set value SC and the LP set value LP, then the following expressions can be obtained: SC = (b1•D100 - a1•D20 - a3•b1 + a1•b3)/(a2•b1 - a1•b2) LP = (b2•D100 - a2•D20 - a3•b2 + a2•b3)/(a1•b2 - a2•b1) If a solid target density and a highlight target density are respectively substituted into D100 and D20 of these expressions, then LP and SC can be determined. On the other hand, when the solid and highlight densities of the case that satisfies the control rule are previously measured, the scoro and LP set values can also be found from these solid and highlight densities. ΔD100 = a1•ΔLP + a2•ΔSC ΔD20 = b1•ΔLP + b2•ΔSC where ΔD100 is a difference between the solid density of the case and the target solid density, ΔD20 is a difference between the highlight density of the case and the target highlight density, ΔLP is a difference between the LP set value of the case and the next-time LP set value, and ΔSC is a difference between the scoro set value of the case and the next-time scoro set value. If these expressions are solved with respect to the difference ΔSC between the scoro set values and the difference ΔLP between the LP set values, then there can be obtained the following expressions: ΔSC = (b1•ΔD100 - a1•ΔD20)/(a2•b1 - a1•b2) ΔLP = (b2•ΔD100 - a2•ΔD20)/(a1•b2 - a2•b1) If ΔD100 and ΔD20 are substituted into these expressions, then ΔLP and ΔSC can be determined, so that the next-time scoro and LP set values can be found. The control rule can be expressed by the coefficients a1, a2, a3, b1, b2 and b3, or by a1, a2, b1 and b2. (5) Basic Operation Next, a description will be given below of the operation of the image forming apparatus after initialized. The operation of the image forming apparatus after initialized, as shown in Fig. 12, consists of an image forming operation (S22 and S23) and an application rule adapting operation (S24). The image forming operation is an image forming operation of an ordinary electrophotographic system, while the application rule adapting operation is an operation to adapt a rule that is applied when controlling an image density. The adapting operation can be performed by forming a patch image and measuring the density of the patch image, which will be described later in detail. In the embodiment 1, since the patch image can be formed simultaneously with a main image (Fig. 4), at the adapting timing, not only the main image but also the patch image are formed (S21 and S22). The adapting timing can be set arbitrarily, for example, it can be set each time a given number of main image prints are formed. In particular, the matching timing can be set for each print or for every 10 prints. Alternatively, the matching timing can be set after passage of a given time, or according to occurrence of a given event. The main image forming operation (S22) is executed as shown in Fig. 13. First, it is checked whether the power has just been made on (S31). Since neither measured solid nor measured highlight density exists immediately after the power-on, a solid density patch and a highlight density patch for setup are formed. As the scoro and PL set values in this case, the values of the previous control (that is, the control just before the power-off) may be used, or a default value may be used. Then, the densities of the formed solid density patch and highlight density patch are measured by a sensor (S33). Next, it is checked whether the density target value is changed by a user or not (S34). If not, then the latent image of the main image is formed by use of the current scoro and LP set values (S37), and the latent image is developed with a toner (S38). If the target value is found changed, then the solid density and highlight density that correspond to their respective target values are computed and differences ΔD100 and ΔD20 between the thus computed solid density and highlight density and the previously measured solid density and highlight density are found. The control rule that is applied to the current status is expressed by the following expressions: ΔD100 = a1•ΔLP + a2•ΔSC ΔD20 = b1•ΔLP + b2•ΔSC By substituting previously obtained ΔD100 and ΔD20 into these expressions, ΔLP and ΔSC are obtained (S35). By subtracting thus obtained ΔLP and ΔSC from the current LP set value LP and scoro set value SC respectively, there can be obtained a new LP set value and scoro set value (S36). A latent image of the main image is formed in accordance with the newly obtained LP set value and scoro set value and the latent image is developed with a toner (S37 and S38). The main image and patch image forming operation (S23) is shown in Fig. 14. This operation is almost the same as the operation shown in Fig. 13 and, therefore, in this operation, the steps thereof corresponding to the steps of the operation of Fig. 13 are given the same numbers and thus the detailed description thereof is omitted here. In short, in the operation to form the main and patch images (S23), the latent images of not only the main image but also the patch image are formed in Step 39. The application rule adapting operation, as shown in Fig. 15, includes a newest control rule correcting operation (S47) for correcting the newest control rule corresponding to the current status, a control rule generating operation (S42) for, after the status is changed, generating a new control rule corresponding to a new status, and an application rule synthesizing operation (S48) for synthesizing an optimum control rule from one control rule or a plurality of control rules that are currently stored. In Fig. 15, first, it is checked whether the status of the image forming apparatus main body is changed or not (S41). Whether the status of the image forming apparatus main body is changed or not is judged in accordance with the time of the image formation and the cumulative number of image prints formed. When a given time has passed, or after a given number of image prints have been formed, there is a high probability that the status of the image forming apparatus main body has been changed and, therefore, these values can be used as substitute. Alternatively, this judgement can be made in accordance with the concrete status quantity of the image forming apparatus main body such as the temperature, humidity and the like, or other substitute values than the status quantity can also be used for this judgement. If the status is found changed, then a rule forming operation (case data and the like stored) is initialized (S41 and S42). In a rule generation step S44, data on cases obtained after the change of the status are stored and, if there are stored data on a sufficient number of cases to generate a rule of a new status, then a new control rule is formed. If the control rule is formed, the rule generating operation is ended, and a control rule generation mode is reset (S45 and S46). If the status is found not changed, then it is checked whether the current operation is in the control rule generation mode or not (S43). If the current operation is found in the control rule generation mode, then the control rule generating operation is executed (S44). If the current operation is found not in the control rule generation mode, that is, when, after the status has changed, a new control rule corresponding to the status has been already formed, then an operation to correct the control rule is carried out (S47). By means of the above operations, each time the status is changed, the control rule generating operation is initialized and, if a sufficient number of cases are stored during the continuation of the status, then a new control rule is generated. That is, normally, there are prepared a plurality of control rules. Alternatively, the greatest number of control rules may be preset and, after the greatest number of control rules are prepared, the control rules may be updated by a given rule. The application rule synthesizing operation (S48) computes the adaptability of the current status and the adaptabilities of the respective control rules, weights the respective control rules according to the thus computed adaptabilities, and then combines together the weighted control rules to thereby synthesize an application rule to be applied to the next and following image generating operations. The adaptabilities can be selected, for example, in such a manner that they become greater as deviations between the densities of patches formed just before the current status and densities obtained when the scoro and LP set values in the patch forming operation are applied to the respective control rules become smaller. For example, if the deviation of the solid density of a control rule Ri (i is a positive integer) is expressed as E100, i and the deviation of the highlight density thereof is expressed as E20, i, then the matching degree of the solid density w100, i and the adaptability of the highlight density w20, i can be expressed in the following manner: w100, i = (1/E100, i)/(Σ (1/E100, j)) w20, i = (1/E20, i)/(Σ (1/E20, j)) where Σ means a sum with respect to j. Thus, the whole adaptability wi is obtained in this manner: wi = w100, i x w20, i. The sum of the adaptabilities wi is computed, and the adaptabilities of the respective control rules are divided by the thus computed sum to thereby find a normalized adaptability Wi. Fig. 16 shows an example in which adaptabilities w100, i of solid case planes of clusters A and B are calculated. In Fig. 16, it is assumed that an actual solid patch density obtained when the current scoro and LP set values are respectively SC and LP is expressed as B0. Also, a corresponding solid patch density of the solid case plane of the cluster A and a corresponding solid patch density of the solid case plane of the cluster B are expressed as B1 and B2, respectively. Thus, deviations E100, 1 and E20, 2 are |B0 - B1| and |B0 - B2|, respectively. Assuming that at the present time there exist only two clusters, calculation is made such that w100, 1 = (1/|B0 - B1|)/(1/|B0 - B1| + 1/|B0 - B2|) and w100, 2 = (1/|B0 - B2|)/(1/|B0 - B1| + 1/|B0 - B2|). Similarly, adaptabilities w20, 1 and w20, 2 of highlight densities are obtained, to thereby find the general or whole adaptabilities w1 and w2. By dividing the adaptabilities w1 and w2 by the sum (wl + w2), normalized adaptabilities W1 and W2 are obtained. As described above, in the present embodiment, each time the status is changed, an operation to generate a new control rule suitable for the changed status starts and, if a sufficient number of cases are prepared, then a new control rule can be generated. Therefore, there is eliminated the need to collect various pieces of data to thereby cope with various conditions properly before the image forming apparatus is shipped, which makes it possible to reduce the cost of the image forming apparatus to a great extent. Also, in the present embodiment, since various kinds of control rules are synthesized together in accordance with the adaptabilities that correspond to the ever-changing conditions, it is possible to cope with various conditions even if the number of control rules is small. In this case, if control rules which correspond to typical statuses are previously input prior to the shipment of the image forming apparatus, then various statuses can be dealt with immediately. In the storage management of the control rules, if these typical control rules are previously processed in such a manner that they are unupdatable, then there is no possibility that such typical control rules can be erased when a new control rule is registered. (6) Detailed Control Flow of Adapting Operation of Application Control Rule Next, a description will be given in detail of a flow for controlling the adapting operation of the application control rule (S24) with reference to Figs. 17 to 22. Firstly, a description will be given below of the whole flow of the adapting operation of the application control rule with reference to Figs. 17 and 18. [Step S101] There is obtained an error E100 between a measured solid density and a target solid density. Similarly, there is obtained an error E20 between a measured highlight density and a target highlight density. [Step S102] A difference between the time and the cumulative number of the previously stored first case and the time and the cumulative number of the current case. [Step S103] It is checked whether the time difference is within 10 minutes or not and whether the difference between the cumulative number is within 20 or not. If the time difference is found within 10 minutes and the cumulative number difference is found within 20, then it is judged that the status is not changed, and thus the processing goes to an operation to correct the current control rule. On the other hand, if the time difference exceeds 10 minutes, or if the cumulative number difference exceeds 20, then it is judged that the status is changed, and thus the processing moves to a mode for generating a new control rule (Steps S104, S105 and S106). [Steps S104 to S106] This is an operation for generating a new control rule upon detection of a status transition. To begin with, cases stored or held before the transition are erased (S104). Next, the current data, time and cumulative print number are provisionally registered in the first one of cases (S105). Then, a cluster generation flag is switched on (S106). Here, the term "cluster" means a group of cases which are detected in one status, and a control rule on this status is generated in accordance with the data on the cases that are contained in the cluster. The fact that the cluster generation flag is on indicates that the current processing is in a mode to generate a new control rule. [Steps S107 to S109] In these steps, if there occurs an outstanding case, this case is added as a case. Here, the term "outstanding case" means a case which must be taken into account in generating a new rule, or a case which must be taken into account in correcting the current control rule. In this instance, the outstanding case occurs when one of the current solid density error and the current highlight density error exceeds an allowable error. First, in Step S107, it is checked whether the current solid and highlight density errors are in the range of the allowable error. As the allowable error, the solid density is classified into 6 levels, while the highlight density is classified into 5 levels. If the density error exceeds the allowable error, then the current data and time are recorded (S108) and, after the data of the cases are stored (S109), the processing advances to a control rule generating and correcting step S110. The recording of the data on the cases will be described later in detail with reference to Fig. 19. On the other hand, if the error is in the allowable error range, then the processing advances to the control rule generating and correcting step S110 without performing any other processing. [Step S110] In the control rule generating and correcting step, if the status is changed, then a new control rule is generated. On the other hand, if the status is not changed, then a control rule formed in connection with this status is corrected. Also, the adaptability on the control rule is computed. The details of the control rule generating and correcting operation will be described later with reference to Figs. 20 and 21. [Step S111] The coefficients a1, a2, b1, and b2 of all control rules Ri are multiplied by the adaptability Wi of the control rules to thereby find sums A1, A2, B1 and B2, and these sums are employed as the coefficients of an application control rule. That is, the correction values ΔSC and ΔLP of the operation quantities are found based on the deviations ΔD100 and ΔD20: ΔD100 = A1•ΔSC + A2•ΔLP ΔD20 = B1•ΔSC + B2•ΔLP If these expressions are solved with respect to ΔSC and ΔLP, then there are obtained the following expressions: ΔSC = (B2•ΔD100 - A2•ΔD20)/(A1•B2 - A2•B1) ΔLP = (B1•ΔD100 - A1•ΔD20)/(A2•B1 - A1•B2) where A1 = Σa1i•Wi, A2 = Σa2i•Wi, B1 = Σb1i•Wi, and B2 = Σb2i•Wi, while Σ means a sum with respect to i. [Steps S112 to S114] It is checked whether a correction value can be obtained or not (S112). That is, if the value of A1•B2 - A2•B1 is 0, namely, when the solid and highlight density planes of the synthesized application control rule are parallel to each other, solutions of ΔSC and ΔLP cannot be obtained; therefore, the correction value is set at 0 and the previous scoro and LP set values are used as they are (S114). On the other hand, when solutions can be obtained, ΔSC and ΔLP are found from the above expressions (S113). [Step S115] The scoro and LP set values are corrected by using the thus obtained ΔSC and ΔLP. Next, a description will be given below of an operation (S109) to store the data on the outstanding case with reference to Fig. 19. [Step S120] It is checked whether the cluster generation flag is on or not. When the cluster generation flag is off, namely, when the status has not been changed, if data of an outstanding case is obtained, then the outstanding case is stored and used to correct a control rule for the current status. [Steps S121 and S122] If the cluster generation flag is on, it is checked whether the number of the outstanding cases stored so far is two or more, or not (S121). If the number of the cases is less than 2, then the processing goes to Step 124, in which the data on the cases are stored. If the number of the cases is two or more, then the processing goes to Step 122, in which it is checked whether a control rule can be computed or not. In the present embodiment, normally, a control rule can be generated if three cases are present. However, when the data on the three cases are arranged on a straight line, a control rule plane cannot be defined and, therefore, the control rule cannot be computed. In such a case, a new case is not stored as a normal case but is stored as a holding case (S123). The data on the holding case can be used as supplementary data when the data on a sufficient number of cases to form a control rule are prepared afterward. [Step S124] An operation quantity (scoro and LP set values) and a control quantity (solid and highlight densities) are recorded. Also, the number of cases recorded is incremented by 1. If there is present a holding case, then it is registered additionally. The registration of the data on the outstanding case is carried out in the above-mentioned manner. Next, a description will be given below of the control rule generating/correcting operation (S110). [Step S130] In the control rule generating and correcting operation, first, the coefficients of the control rule of the current status are computed. This will be described later in detail with reference to Fig. 22. If the current control rule has been newly generated or corrected, a write flag is set at 1 and, if not, then the write flag is set at 0 (see Step S153 shown in Fig. 22). [Step S131] It is checked whether the write flag is on or not. If it is found on, which means that a new control rule has been generated for the current status or the previous control rule has been corrected for the current status, different operations are performed for the formation of the new control rule and the correction of the previous control rule. If the write flag is off, then the processing advances directly to Step S140 without performing any other operation. [Step S132] It is checked whether the cluster flag is on or not. If on, that is, if a new control rule is to be generated, then the processing goes to Step S133. If the cluster flag is off, that is, if the previous control rule is to be corrected, then the processing advances directly to Step S137. [Step S133] It is checked whether the number of outstanding cases is three or more, or not. If it is less than three, then a new control rule cannot be formed and thus the processing goes to Step S137. If the number of outstanding cases is three or more, then a control rule can be formed and thus the processing advances to Step S134. [Steps S134 to S136] In Step S134, the cluster number or the rule number is incremented by 1. Next, in Step S135, the date and time of the first case in the cluster are registered into the newest cluster (control rule) and further, in Step S136, the cluster flag is switched off. [Steps S137 to S139] The newest cluster (the newest control rule) is registered and updated using a control rule calculated in a rule calculating step (S137). Then, the cumulative number is registered into the newest cluster and the write flag is switched off (S138 and S139). [Steps S140 to S146] In this series of steps, for a plurality of control rules, their adaptabilities are obtained and an application control rule is synthesized according to the thus obtained adaptabilities. If there exists only one control rule, then the control rule, as it is, is used as an application control rule. First, the current scoro and LP set values are applied to the respective control rules to thereby compute the solid and highlight densities of the respective control rules. Then, deviations between the thus computed solid and highlight densities and the actually measured solid and highlight densities are computed (S140). The deviation between the solid densities of each control rule is expressed as E100, while the deviation between the highlight densities is expressed as E20. Next, the deviation E100 between the solid densities of each control rule is divided by the smallest solid density deviation. Similarly, the deviation E20 between the highlight densities of each control rule is divided by the smallest highlight density deviation (S141). Then, there is obtained the sum of the reciprocals of the quotients with respect to the solid density deviations, and the reciprocals of the quotients are divided by the sum, i.e., normalized. These are referred to as the contribution factors of the respective control rules with respect to the solid densities. Similarly, with respect to the highlight density deviation, there is obtained the sum of the reciprocals of the quotients, and the reciprocals of the quotients are divided by the sum, i.e., normalized. These are referred to as the contribution factors of the respective control rules with respect to the highlight densities (S142). Then, for each of control rule, the contribution factors of the solid density and the highlight density are multiplied together, and the resulting product is considered a contribution factor of the control rule (S143). Next, the contribution factor of each of control rule is divided by the largest contribution factor, and the resulting quotient is divided by the sum of the quotients, i.e., normalized (S144 and S145). The thus obtained values are stored as the adaptabilities Wi of the respective control rules Ri. In the above-mentioned manner, the adaptabilities of the control rules are computed and the application control rule is obtained. That is, the coefficients a1, a2, b1 and b2 of all control rules Ri are multiplied by the adaptabilities of the control rules to thereby obtain the sums A1, A2, B1 and B2, which are employed as the coefficients of the application control rule. A1 = Σwi•a1i A2 = Σwi•a2i B1 = Σwi•b1i B2 = Σwi•b2i where Σ is a sum with respect to i. Next, a description will be given below of a control rule coefficient computing operation (S130) with reference to Fig. 22. [Step S150] To begin with, it is checked whether there are present three or more pieces of case data or not. If the number of the case data is less than 3, then it is impossible to compute the coefficients of a control rule and thus the computing processing is ended. If the number of the case data is three or more, then the processing goes to Step S151. [Step S151] The optimum control rule coefficients a1, a2, a3, b1, b2 and b3 are computed according to the method of least squares. [Step S152] It is checked whether a1·b2 - a2·b1 is 0 or not. If it is 0, then the control planes are parallel to each other and thus the scoro and LP set values cannot be computed and, therefore, without employing the coefficients of such control rule, the control rule computation is ended. [Step S153] If a1·b2 -a2·b1 is not 0, then these coefficients can be employed as the coefficients of the control rule and, therefore, the write flag is switched on to thereby end the control rule coefficient computing operation. (7) Advantages of Embodiment 2 (a) In this embodiment, by using the above-mentioned control cases, there can be realized a control method which does not use other physical quantity sensors than the density sensor and is independent of the previous data collection and the analysis thereof by engineers. This can reduce the number of sensors used and man-hours necessary for development, which in turn can reduce the manufacturing cost of the image forming apparatus. On the other hand, according to the prior art, since there is employed a control method based on a physical mechanism, when trying to perform a similar control operation to the present embodiment, physical quantities such as a charged potential, an exposure light potential and the like are monitored by a potential sensor or the like, a developed potential (a difference between the exposure light potential and a developing bias) and a cleaning potential (a difference between the charged potential and the developing bias) are obtained from the thus monitored physical quantities and the developing bias set values, the optimum developing potential for realizing the target solid density is computed according to a relationship between the previously data collected solid density and the developing potential, a variation in the highlight density caused by changing the developing potential into the optimum developing potential is computed, and a highlight density error to be corrected with the highlight density variation taken into account is computed according to a relationship between the previously collected data on highlight density and the cleaning potential, thereby determining the charging potential and exposure light potential. In accordance with not only a relationship between the previously collected data on charging potential and the scoro set value but also a relationship between the exposure light potential to be applied to the last-mentioned charging potential and the LP set value, LP and scoro set values to be set in the next image forming operation must be determined. Also, the previous data collection must be executed under various temperature and humidity environments since the electrophotographic system depends on the temperature and humidity. (b) In the present embodiment, with respect to the status that the image forming apparatus is encountering, a substitute value (a sampling time) can be used for the status quantity and, therefore, it is not always necessary to sense the status quantity. This means from the viewpoint of the physical mechanism for image formation that even an entirely black box can be controlled. That is, it can be well said that the present invention can also apply effectively in other systems than the electrophotographic system. Also, as another operation of the present embodiment, it is possible to employ an arbitrary parameter as a controlling actuator. That is, according to the prior art, there are present parameters which cannot be detected due to no existence of a proper sensor or cannot be used from the viewpoint of costs. However, the present invention is able to deal with its set values directly and, therefore, it is able to select the set values or such parameters arbitrarily and use them for control. (c) Further, as described before, simply by inputting a sufficient number of cases to determine the control planes, the present embodiment can be set up (initialized). That is, the present embodiment provides an action that there is eliminated the need for provision of special technology and devices necessary to set up or initialize the image forming apparatus. According to the present embodiment, there can also be provided another action that, even if the cases happen to deviate greatly from their respective target densities, such deviation has no ill effect on the control performance of the image forming apparatus. This is because the present image forming apparatus per se is always able to extract the control rule that matches a new cluster, namely, a new status as the occasion demands. Referring to the comparison of the above action with the prior art, for example, in a conventional control rule learning technique based on a neural network, if there are not prepared the optimum data as teachers' data, then the reasoning performance of the neural network is impaired and additional learning and re-learning cannot be carried out automatically, so that a sufficient control performance cannot be obtained. Or, in another conventional technique based on a fuzzy theory, if a trial-and-error tuning operation by an engineer is not carried out in the most suitable manner, then a sufficient control performance cannot be obtained. These comparisons show that the action of the present embodiment is fur better than the conventional techniques.Further, in the present embodiment, even if the image forming apparatus encounters for the first time such a status that it has never experienced, it is able to extract a new control rule adaptable to such environment or status simply by carrying out a given number of printing operations and, if the image forming apparatus encounters a similar environmental condition after then, then it is able to cope with or control such environmental condition immediately. Therefore, the present image forming apparatus can deal with the change of the status with the passage of time without collecting data previously. That is, even if the status change with the passage of time occurs, the present image forming apparatus can always continue to follow the changing status.This action provides a great effect that there is completely eliminated the need to perform a conventional developing operation in which scores of thousands of or hundreds of thousands of printing operations must be executed to collect previous data on the changes with the passage of time.Also, according to the prior art, even the thus collected data on the changes with the passage of time cannot be always used effectively in all of image forming apparatuses because the image forming apparatuses are different from one another. Further, according to the prior art, there is left another problem to be solved: that is, since a user uses the image forming apparatus under a different condition from the previous data collected condition by a maker, namely, since the image forming apparatus is used under such condition that is not expected by the maker, there occur such changes that are different from the maker's expected changes with the passage of time, which makes the control rule unapplicable, so that the image density cannot be controlled to a desired value. On the other hand, according to the invention, there are eliminated the previous data collection and any special means for the inter-apparatus difference, and, there can be provided an action which is able to cope with every image forming apparatus individually and perfectly under any users' environments to thereby solve the density variations caused by the changes with the passage of time.From this action, there is also obtained another action: that is, for example, even when element parts such as a photoreceptor, a developer and the like having significant influences on the image density are replaced, a desired image density can be automatically obtained in correspondence to the new element parts simply by executing a given number of printing operations. This is an operation which, according to the prior art, has been performed by an operator such as a service engineer and, therefore, according to this aspect of the present embodiment, there can be provided a labor cost reducing effect. Also, according to the present embodiment, even if an ordinary user, not an expert such as the service engineer, replaces the parts, the optimum image forming status can be obtained automatically. That is, the present embodiment can be handled easily by everyone.Moreover, according to the present embodiment, there is provided another action in which, when coping with the new condition in the above-mentioned manner, by using a concept "adaptability" in a plurality of past clusters, the new control cases need not be always stored. That is, due to this action, the present embodiment is able to deal with the new condition immediately without waiting for the completion of a given number of printing operations, or with no need for provision of a memory which stores new control cases additionally. (d) In this embodiment, if the elapsed time and the cumulative number of prints are respectively within given ranges, then the status is judged to be left unchanged and thus newly obtained cases are used to correct the control rule of the current status. That is, if substantially physical changes have not occurred but the adaptability of the control rule has not been good simply due to the poor accuracy (such as large measurement error) of the past cases, then it is more effective to use together the past and new cases to thereby reduce the effects of the measurement errors that the individual cases statistically have. For example, it can be expected that a more accurate control rule can be expected by applying a statistical computation method such as the method of least squares or the like to a larger number of cases to thereby reduce the errors rather than by determining the control case planes from a given number of cases. On the other hand, if the elapsed time and the cumulative number of prints respectively exceed the given ranges, then the status is judged to have changed and thus a control rule on a new status can be formed using the cases obtained. Employment of the control case depends solely on whether the control result of thereof obtained by using the control case planes satisfy the above-mentioned allowable error. That is, if the result of the control case satisfies the allowable error, then it is considered that there has been obtained a control rule which is capable of a sufficiently accurate control. If it does not satisfy the allowable error, then it is judged that it is necessary to increase the control cases in number to thereby enhance the accuracy of the control case planes.As described above, in the present embodiment, if the status remains unchanged, then the current control rule is corrected using the cases; and, if the status has changed, then a control rule suitable for the new status can be newly generated. That is, a suitable control can always be carried out. (e) Further, in this embodiment, there can be provided another action in which the control accuracy itself can be set arbitrarily. That is, the allowable error with respect to the target density can be set immediately and the improvement and correction of the control rule or the formation of a new control rule can be carried out in accordance with the set allowable error, so that necessary and sufficient control accuracy can be achieved automatically. Further, if there is obtained such necessary and sufficient control accuracy, then it is not necessary to store more control cases than necessary and too much memory capacity is not required. (f) Still further, in the present embodiment, there is provided another action in which various control systems can be employed according to the characteristics and objects of the image forming apparatus since an arbitrary value can be used as the status quantity or its substitute value. In other words, there is completely eliminated a conventional developing operation to build individual control algorithm even if the contents to be currently controlled are different from each other, for example, when the daily variations are to be controlled (the status quantity is classified by the data) or when the density variations mainly caused by the cycle-down/cycle-up are to be canceled (the status quantity is classified by the number of prints formed after a start button is depressed). Of course, even when trying to recognize the status by using a temperature sensor and a humidity sensor similarly to the prior art, the present embodiment can be applied as it is with no alteration. (g) Yet further, according to the present embodiment, there can be provided another action in which the memory capacity can be used most effectively. That is, the data are erased automatically in the increasing order of importance or in the decreasing order of the storage time and, therefore, even if the memory capacity is limited, it is possible to achieve the control that matches the then status. Also, since the data on the cases are used only for correcting the newest control rule or generating a new control rule, only the data on the newest cluster need to be stored. For this reason, it is not necessary to store the data on the cases so much but a correspondingly larger number of control rules can be stored. (8) Modification of Embodiment 2 The second embodiment can be modified in various manners as described below. (a) In the above embodiment, the image output part IOT is a laser printer of a single color. However, the invention is not limited to this but the invention is also able to provide entirely the same effect even if a multi-color laser printer or a copying machine of an analog type is used as the image output part IOT. Further, the image output part IOT is not limited to the above-mentioned electrophotographic system but the invention can also be applied to an image output part of an ink jet system or other similar system. (b) The sensor used in the present embodiment is just an example and, to obtain the effects of the invention, any other type of sensor can also be used, provided that it is able to measure the density of a developed patch. Also, as an object to be monitored, any object can be used, provided that it has a high correlation with the final image density; for example, any of a developed image, a transferred image and a fused image can be monitored, provided that it can be made to correspond to the final image density that is given to a user. (c) In the above embodiment, as the density of the developed patch, there are employed two kinds of patch densities, that is, the solid (dot coverage of 100%) density patch and the highlight (dot coverage of 20%) density patch. However, the density of the developed patch is not limited to these two kinds of densities but, for example, only the density that corresponds to a dot coverage of 50% can be controlled, or, by using a larger number of patches, a larger number of gradation points can be controlled. However, when the respective gradation points are to be controlled independently of each other, it is necessary to prepare controlling parameters in such a number as corresponds to the number of the gradation points. (d) In the above embodiment, the density of the developed patch is monitored. However, this is not limitative but the reproduced image density may also be monitored directly, or other physical quantity may also be monitored which can be used as a substitute for the developed patch density. (e) In the above embodiment, the developing bias set value is used as a fixed value but, alternatively, the grid voltage set value of the scorotron charger and the developing bias can also be used as control parameters. This is because the developing bias also has a high correlation with respect to the solid density and highlight density. Therefore, as another combination, the grid voltage set value of the scorotron charger may be fixed, while the laser power set value and developing bias may be employed as control parameters. Alternatively, it is also possible that the laser power set value, the developing bias set value and the grid voltage set value of the scorotron charger are used to control three gradation points. That is, for example, the dot coverage may be set as 100%, 50% and 20%. (f) The above embodiment has been described on condition that the two-component developing system is used. In this case, the toner density in the developer, namely, the mixture ratio of the toner and carrier relates to the developing density, while the toner density is separately controlled to a generally given toner density according to a technique in which the quantity of supply of a toner is adjusted to be proportional to the pixel number of an image to be output. Or, as a further technique, the toner density may be controlled to such generally given toner density by monitoring it by use of a sensor of a magnetic type or an optical type which can be obtained on the market and has been conventionally used. Also, in the present embodiment, since it is not necessary that the toner density is variably controlled positively to thereby control the image density to a desired value, it is sufficient that the toner density is maintained at the generally given toner density. This is because, even if the toner density varies a little, such variation can be absorbed by setting the above-mentioned parameters (scoro set value, LP set value).On the other hand, when a one-component developing system is used, the toner density is always 100% and thus has no direct effect on the image density. Therefore, the toner density can be sufficiently controlled by a conventional toner quantity control technique such as a developer blind detection control technique or the like. (g) In the second embodiment, as the status quantity, the time and the cumulative number of image formed prints are used. In this manner, as the status quantity relating to the image density, it is not always necessary that any physical quantity is sensed, but an arbitrary status quantity or a substitute value for the status quantity can be used according to the characteristics of the assumed image forming apparatus. When there is room for the cost or the mounting space for the sensor, that is, when the temperature or humidity sensor is used to grasp the status quantity more accurately to thereby enhance the control performance, of course, a sensor for sensing another status quantity can also be provided as the occasion demands. Even in such case, it is not necessary at all to add a special processing or change to the present invention.For reference, according to our experiments, even if the temperature or humidity sensor is not used directly, the control cases can be eventually classified according to the temperature variations or humidity variations and the control rules respectively corresponding to the control cases can be formed automatically. Therefore, when an ordinary image forming apparatus is used, it is not necessary to use a special temperature sensor or a special humidity sensor. (h) In the second embodiment, the control case planes are used, while this is not limitative but, alternatively, instead of the planes, a curved surface (including a polyhedron as well) can be used. Next, a description will be given below of a control rule obtaining method which is different from the present embodiment. That is, instead of the above-mentioned "planes" obtained from the control cases, a "curved surface" higher in the degree than the plane can also be used. When such a polyhedron as shown in Fig. 23 is used, the polyhedron corresponds to a case in which a plurality of control case planes are used with a certain condition given thereto. That is, only in the area that can be covered by a given control case plane, this plane is applied and, in the area that goes beyond the above area, another control case plane is applied. The application areas of the respective control case planes are alternatively allotted. Also, in Fig. 24, the polyhedron of the control case, for the purpose of easy understanding, is shown with the number of dimensions reduced by one (that is, a plane is expressed as a straight line, while a curved surface is expressed as a curved line). Generally, between a control rule and its adjoining control rule, synthesis is made according to the "adaptabilities" of these two control rules and, therefore, for example, at the just middle position of the two control rules, the adaptabilities of the two control rules are respectively 50%. Accordingly, planes respectively having an average inclination of the inclinations of the two control rules are formed virtually and are also moved parallel so that they can coincide with the actual physical phenomenon. In other words, the above description shows that the present control rule generating method is able to perform a control operation in the same condition as a smooth curved surface exists.A control case curved surface is able to represent a plurality of control case planes or a control case polyhedron but, to obtain the control case curved surface, a larger number of control cases are necessary and thus it takes much time to form a control rule. For example, if a solid case curved surface is expressed as a1•LP 2 + a2•LP + a3•SC + 4, then there are obtained coefficients a1 - a4 which minimize the sum of squares of (a1•LP2 + a2•LP + a3•SC + a4 - D100) with respect to the respective cases. The coefficients of the control case curved surface can be determined by use of a numerical analysis program.As can be seen from the above description, there can be expected two kinds of control rule generating methods: that is, in one method, a control rule is determined quickly by use of a simple plane and a large number of planes are combined together as the occasion demands; and, in the other method, from the beginning, a highly accurate control rule is expressed by a higher degree curved surface and the number of curved surfaces is reduced. Which one of the two methods is selected depends on what control characteristics are desirable to an image forming apparatus assumed or a user. The present invention can be applied to either of the two methods.When the case control plane or curved surface is determined according to a statistical method, not only the mean square method but also other computing methods such as a mean method and the like can also be used. (i) The developed patch can be formed and sensed entirely in the same manner as in the prior art and no special restrictions are necessary in constituting the present invention. That is, as in the conventional method, the patch may be formed each time an image is formed, or it may be formed only before or after a series of jobs. Or, the patch may be formed for each given number of prints or each given time. Generally, the formation and detection of the patch is advantageous in that the reproduced status of the image density can be grasped more accurately as the patch forming frequency increases. However, it is also disadvantageous in that it consumes a larger quantity of toners. In view of this, the optimum patch forming frequency may be employed according to the specifications and objects of the image forming apparatuses used. (j) In the memory management in second embodiment, the data are erased in a decreasing order of the storage time. However, with respect to the control rule, each time it is used for control, the adaptability thereof may be accumulated and whether the control rule is erased or not may be determined by use of the resultant cumulative adaptability. That is, since a control rule having a small cumulative adaptability is a control rule which is used with little frequency, the control rule may be erased in an increasing order of frequency of use. Next, this erasing method will be described below in more detail. First, it should be noted here that, when judging the importance of the control rule, it is not always good to judge it only based on whether the control rule is new or old. The reason for this is as follows: since an image forming apparatus using an electrophotographic process is greatly influenced by the temperature and humidity environment, for example, when a relation between the image forming apparatus and the four seasons in Japan is taken into account, there is a possibility that, in performing a control operation in summer, a control rule extracted last summer can apply more effectively (that is, the control rule has been stored about one year) than a control rule extracted last winter (that is, the control rule has been stored about a half year).Therefore, the importance of the control rule should not be judged simply based on whether the data on the control rule is new or old, but the control rule importance should be judged accurately according to some other suitable method. In view of this, each time the control rule is applied, the adaptability of the control rule is accumulated and the control rule importance is judged using the resultant cumulative adaptability.However, in this case, if the control rule importance is to be judged only in accordance with the cumulative adaptability, then there arises a problem. That is, the newer a control rule is (it is not long after it is formed), the smaller the cumulative adaptability of the control rule is; and, the older a control rule is, the more frequently the control rule is used even if the control rule has an adaptability for each use (thus, even if the importance of the control rule is low), which increases a possibility that the cumulative adaptability of the old control rule can become large. On the other hand, external environments such as temperature and humidity having a great influence on the image forming apparatus vary greatly according to the four seasons, especially in Japan. Therefore, it is desirable that the cumulative condition of the adaptabilities is taken into account over a period during which the similar environmental conditions of the season, in which the subject control rule is extracted, continue.For example, it is assumed that a control rule has been existing 3 months in a similar environmental status to the status of the control rule at the time (in the season) when the control rule was extracted, and, among the control rules that have been existing three months or longer since they are formed, a control rule having the smallest cumulative adaptability is regarded as the control rule of the least importance and is thus erased. This way of erasing eliminates the possibility that only the newly extracted control rule can be erased thoughtlessly. That is, if a new control rule has been existing for 3 months or less since it was extracted, then the new control rule is usually a rule of less frequency and of a small adaptability but, however, according to the above way of erasing, there is eliminated the possibility that such new control rule can be erased thoughtlessly.Referring in more detail to this way of erasing, if a control rule is expressed by a primary approximation according the least square method, then the elements of a control rule (or cluster) are stored in such a manner as shown in Fig. 25. That is, the elements of a control rule shown in Fig. 25 includes coefficients a1, a2, a3, b1, b2, and b3 to approximate the solid and highlight densities as follows: (solid density) = a1 x (LP set value) + a2 x (scoro set value) + a3 (highlight density) = b1 x (LP set value) + b2 x (scoro set value) + b3 The elements further includes the year, month, date, hour, minute, and second at which the control rule is formed, that is, the time of occurrence of the last (the newest) control case of a group of control cases from which the present control rule is extracted, and the cumulative adaptability. Each time the control rule is used for control and the adaptability thereof is computed, the thus computed adaptability is added to the cumulative adaptability.Fig. 25 corresponds to Fig. 7 and shows an example in which control rule-1 is extracted from cases 1 - 3 and control rule-2 is extracted from cases 4 - 6.Since the control rules (or clusters) are described according to the above technique, when the memory residual quantity is running short, first it can be checked, in accordance with the year, month, date, hour, minute and second of the extraction of the control rule, whether the control rule has existed for 3 months or longer or not. Next, if it is found that the control rule has existed for 3 months or longer, then the cumulative adaptability of the present control rule is compared with the cumulative adaptabilities of the other control rules that have been existing for 3 months or longer to thereby be able to judge the importance of the present control rule.When the previously prepared memory capacity is small or when new control rules are formed very frequently, there is a possibility that the memory residual capacity can run short before the elapsed time of the control rule reaches 3 months. In this case, as described before, the control rule is erased simply in a decreasing order of the elapsed time. Therefore, in any case, according to the present embodiment, the image forming apparatus can be structured in such a manner that it can secure a memory for storing the newest data. (k) When finding the adaptabilities of control rules and synthesizing them together in the control rule retrieval unit, a control rule having an adaptability which is less than a given value (10% or 20%) may be neglected, while the adaptabilities of the remaining control rules are obtained again and the thus obtained adaptabilities may be synthesized together for control of the image density. This way of controlling the image density is free from the influences of the control rules having little connection with the image density and, therefore, is able to carry out a more accurate image density (l) In the above embodiment, the image density is the target to be controlled. However, this is not limitative but, for example, the line width, sharpness, gradation or the like may be employed as the target to be controlled. Next, by way of further embodiments that will deal with cases where the status changes, a description will be given of ways of taking cases to be used to form a control rule. More specifically, there will be described an embodiment in which, in the neighborhood of an allowable value range set for a control quantity, the control quantity changes from outside the allowable value range into within the allowable value range, and another embodiment in which the control quantity changes from within the allowable value range to outside allowable value range. Embodiment 3 The present embodiment is different from the second embodiment in a method for comparing the control quantity with the target quality to thereby find a control case. (1) Configuration of Control Part Now, Fig. 26 is a block diagram showing the configuration of the control part of this embodiment of the invention. In Fig. 26, the parts thereof equivalent to Fig. 6 are given the same designations and thus the description thereof is omitted here. In Fig. 26, a convergence judging part 43 detects the output of a density comparator 24 when a difference between the control quantity (density) of the previous print and a target quality (a density set value) is equal to or more than an allowable value, and supplies a convergence detection signal to a status quantity comparator 26 when the difference changes from a value equal to or more than the allowable value to a value less than the allowable value. In particular, if the difference (the density difference) is more than the allowable value, that is, if the density comparator 24 issues an output, then "0" is stored. On the other hand, if the difference is less than the allowable value, that is, if the density comparator 24 issues no output, then "1" is stored. If the difference changes from "0" to "1", then the convergence judging part 43 judges that the difference has converged, and supplies the convergence detection signal to the status quantity comparator 26. In this manner, the convergence judging part 43 of this embodiment detects a change in the status, namely, from the status in which the difference exceeds the allowable value to the status in which the difference decreases or converges into within the allowable value. Also, the density comparator 24 supplies the output (the control quantity) of the developed density sensor 10 to the control case memory 25 also when the difference changes from the value exceeding the allowable value to the value less than the allowable value. Therefore, not only when the difference exceeds the allowable value but also in other case, that is, when the difference changes from the status of more than the allowable value to the status of less than the allowable value, the then case (status quantity, operation quantity, and control quantity) is stored into the control case memory 25. Also, similarly to the above-mentioned second embodiment, the status quantity comparator 26, in accordance with the case that is newly written into the control case memory 25, compares the newest cluster with the time, checks whether the status is similar or not and, if the status is found not similar, then, the status quantity comparator 26 forms and classifies a new cluster. On the other hand, when the convergence detection signal is supplied thereto from the convergence judging part 43, then the status quantity comparator 26 writes the then case into the cluster memory 27 so that this case can be added as the control case of the newest cluster (the last or previous cluster). Responsive to this, the control rule calculating unit 28 computes such a case plane as contains therein the newly added control case and then transfers the case plane to the control rule memory 29. Here, a description will be given below of a difference between the above-mentioned second embodiment and the present embodiment with reference to Figs. 27 to 30. In the second embodiment, as shown in Fig. 27, the density measured value (control quantity) is compared with the density set value (operation quantity) by the density comparator 24 and, only when a difference between them exceeds the allowable value, the then case is added as the control case of the newest cluster, or it is classified as the case of a new cluster according to the number of cases. For example, in an example shown in Fig. 28, since the first two cases exceed the allowable value, if the number of the cases thereof is sufficient (for example, 3 cases), they are classified into the new cluster but, however, in the example shown in Fig. 28, they are not classified because the number of the cases thereof is two. On the other hand, in the present embodiment, as shown in Fig. 29, in accordance with the comparison results by the density comparator 24, not only when the difference between the density measured value (control quantity) and the density set value exceeds the allowable value range but also when the difference goes beyond the allowable value range and after then goes within the allowable value range, the cases thereof are added into the newest cluster. For example, in an example shown in Fig. 30, since the first two cases exceed the allowable value range, "0" is stored for each of the cases, while "1" is stored for the third case because it is included in the range of the allowable value. In the third case, if the status thereof changes from "0" to "1", then the then case (the third case) is added to the newest cluster. As described above, in the second embodiment, since no control case is obtained as far as the control quantity does not exceed the allowable value range, especially after the status is changed, when the control quantity goes within the allowable value range before control cases necessary to form a new cluster are collected, a control case having information on the actual status transition is not classified immediately as a cluster. In this case, no control rule is extracted and, therefore, a control rule experienced in the past must be used. On the other hand, in the present embodiment, even in the above-mentioned instance, a control case having information on the actual status transition can be taken into the cluster. (2) Operation Next, a description will be given below of the operation of the present embodiment having the above-mentioned structure. The operation of the present embodiment will be described in comparison with the previously described second embodiment. Fig. 31 shows a conceptual view of a method of obtaining a control case according to the second embodiment, while Fig. 32 shows a conceptual view of a method of obtaining a control case according to the present embodiment. In the second embodiment, as shown in Fig. 31, only when the comparison result between the control quantity and target quality (density set value) goes beyond the preset allowable value range, the control case is taken. Specifically, the control quantities that are circled in Fig. 31 are taken as control cases. Here, the first to third prints are obtained as the control cases and are classified into a cluster and, when the third control case is obtained, a control rule is extracted from this cluster. After the seventh print shown in Fig. 31, if the status such as the environment and the like changes, then the 8th and 9th prints, of course, are obtained as control cases and are classified into the cluster. However, the 10th print is not taken into the cluster as a control case, because the comparison result between the control quantity and target quality does not exceed the allowable value range. Therefore, at the time of the 10th print, a control rule cannot be extracted yet but an operation quantity for the next control is determined from the existing control rules that have been stored until then. As in the 11th print, when the comparison result between the control quantity and target quality goes again beyond the preset allowable value range, the 11th print is taken into the cluster as a control case, with the result that a control rule can be extracted at last. Here, also in the 11th print and in its following prints formed after the 11th print, when the comparison result between the control quantity and target quality does not exceed the preset allowable value range, then data on the 8th and 9th cannot be used in the just following control because a control rule cannot be extracted immediately with respect to the change in the status such as the environment and the like that is caused just after the 7th print. On the other hand, in the present embodiment, not only when the control quantity goes beyond the preset allowable value range, but also when the control quantity of a certain (the previous) print exceeds the allowable value range and the control quantity of the current print goes within the allowable value range, a control case can be obtained. In particular, in Fig. 32, since the control quantities of the 1st to 3rd prints exceed the preset allowable value range, the convergence judging part 43 stores "0" for the 1st to 3rd prints. The status quantity comparator 45, similarly to the second embodiment, takes the 1st to 3rd prints as control cases and classifies them as a cluster because the control quantities of the 1st to 3rd prints are greater than the preset allowable value range, and thus a control rule is extracted by the control rule calculating unit 28. The convergence judging part 43 stores "1" for the 4th print because the control quantity of the 4th print goes within the allowable value range. Also, the convergence judging part 43 detects that "0" has changed to "1" and thus supplies a detection signal to the status quantity comparator 26. In response to this, the status quantity comparator 26 obtains as a control case the control quantity of the 4th print that exists within the allowable value range, and takes the control case into the cluster. That is, when compared with the second embodiment, according to the present embodiment, there can be obtained a larger quantity of information necessary to determine the operation quantity and, even when the number of control rules is the same, there can be provided a larger control quantity (a larger number of control cases) to constitute a cluster from which the control rules are extracted, so that the accuracy of the thus extracted control rules can be enhanced. Then, the convergence judging part 43 stores "1" for the 5th and 6th prints because the control quantities of these prints exist within the allowable value range. For the 7th and its following prints, even when the status such as the environment and the like is changed, the convergence judging part 43 stores "0" for the 8th and 9th prints because the control quantities thereof exceed the allowable value and, similarly to the second embodiment, takes these control quantities as control cases and takes them into the cluster. For the 10th print, since the control quantity thereof exceeds the allowable value, the convergence judging part 43 stores "1" and, at the same time, detects the change from "0" to "1" and thus supplies a detection signal to the status quantity comparator 26. Responsive to this, the status quantity comparator 26 obtains as a control case the control quantity of the 10th print existing within the allowable value range and takes it into the cluster. In this manner, since the control quantity of the 10th print is taken in as the control case, at this time, that is, at a time when the three control cases are obtained, a control rule can be extracted so that the present embodiment is able to respond immediately to the transition of the status such as the environment that is caused at the current time. Similarly, the control quantity of the 12th print is taken as a control case and is thus taken into the cluster. The number of clusters in the range of 8th - 12th prints of the present embodiment is the same as the second embodiment but, however, the number of control cases taken into the clusters is larger than that of the second embodiment. For this reason, the accuracy of the control rule extracted in the range of 8th to 12th prints is improved over that of the second embodiment. In the above description, a control rule is extracted when three control cases are obtained. However, this is not limitative but the number of the control cases may be selected arbitrarily. (3) Advantages of Embodiment 3 While the configuration and control procedures of the present embodiment have been described above, a description will be given below of the actions and advantages of the present embodiment. (a) After control cases are obtained just after the status is changed, and, especially before there are collected a necessary number of control cases to form a new cluster, even if the control quantity goes within the allowable value range, the control cases that have information on the actual status transition can be taken into the cluster. (b) Therefore, when a control rule is extracted, the optimum operation quantity can be determined by use of not only the control rule extracted in the past but also the newest control rule having information on the current status. (c) Further, since the number of the control cases taken into the same cluster is larger, the accuracy of the control rule that is extracted on the basis of the subject cluster can be enhanced. Embodiment 4 A description will be given below of a fourth embodiment of the invention. The present embodiment is different from the previously described second embodiment in a method for forming a cluster in accordance with a comparison result between a control quantity and a target quality. (1) Configuration of Control Part In Fig. 26, a non-convergence judging part 43 detects the output of the density comparator 24 when a difference (a density difference) between the control quantity (density) of the previous print and a target quality (a density set value) is larger than an allowable value and, when the difference changes from within the allowable value range to outside the allowable value range, supplies a non-convergence detection signal to the status quantity comparator 26. In particular, when the difference (density difference) is larger than the allowable value, that is, when the density comparator 24 issues an output, then "0" is stored; and, on other hand, when the difference is within the allowable value range, that is, when the density comparator 24 issues no output, then "1" is stored. Also, when the status is changed from "1" to "0", then the non-convergence judging part 43 supplies a non-convergence detection signal to the status quantity comparator 26. In this manner, the non-convergence judging part 43 in the present embodiment, oppositely to that of the third embodiment, detects a change in the status in which the density difference has changed from within the allowable value range to outside the allowable value range. Also, when the above non-convergence detection signal is supplied to the status quantity comparator 26, then the status quantity comparator, in accordance with the cases that have been newly written into the control case memory 25, forms a new cluster and classifies the cases into the new cluster. At this stage, the control rule calculating unit 28 calculates a case plane containing therein a newly added control case and transfers coefficients representing the subject plane to the control rule memory 29. Here, a description will be given below of a difference between the previously described second embodiment and the present embodiment with reference to Figs. 33 to 39. In the second embodiment, as shown in Fig. 33, a difference between a measured density value (control quantity) and a set density value is compared by the density comparator 24 and, only when the resultant difference between them goes beyond the allowable value range, the control quantity is added as the control case of the same newest cluster that is formed last. For example, in an instance shown in Fig. 34, the first two cases are classified into the new cluster because the differences thereof exceed the allowable value range. When the control quantity of the third case converges into within the allowable value range and the control quantity of the fourth case goes again beyond the allowable value range, then the control quantity is classified into the last formed newest cluster. At this status, that is, when the control cases become three in number, then a new control rule is formed. Also, in the second embodiment, as shown in Fig. 35, in order to cope with a change in the status such as the environment and the like, the number of prints and the working time are employed as the status quantity and, if, in accordance with the comparison results obtained respectively by an accumulated-number-of-prints comparator 24a and a time comparator 24b, it is found that the number of prints formed exceeds a preset number of prints or the working time goes beyond a preset time, then a new cluster is formed and the control cases that are to be obtained after then are taken into the new cluster, so that they can be stored separately from the control cases that have been obtained until the current status. For example, in an instance shown in Fig. 36, the first and second control cases are classified into a cluster. In Fig. 36, as the third control case, there is shown a status having a converged control quantity, that is, a status the control quantity of which goes into within the allowable value range. However, this is not limitative but another status the control quantity of which does not converge into the allowable value range is similar in operation to the converged control quantity, as will be discussed below: That is, if either of the print number or working time of the above third control case goes beyond either of values previously set for them respectively (the values within which the status are expected to vary during printing are previously set as set values respectively with respect to the print number and working time), then it is judged that the status has changed. Therefore, at the fourth print in which the control quantity goes beyond the allowable value range with respect to the target quality, a new cluster is formed and the fourth control case is classified into the newly formed cluster that is different from the cluster in which the above-mentioned first and second control cases are classified. In this manner, in the second embodiment, a larger number of clusters are formed, control rules are extracted in the respective clusters, the adaptabilities of the respective control rules with respect to the current status are judged, and the control rules are weighted accordingly, thereby operating a new and more accurate control quantity so that the control quantity can provide a value which corresponds to the target quality. However, in this method, a value (the number of prints or the working time), within which the status is expected to vary during the printing operation, must be given as a set value previously. On the other hand, according to the present embodiment, as shown in Fig. 37, the non-convergence judging part 43, in accordance with the comparison result by the density comparator 24, judges that a difference between the density measured value (control quantity) and the density set value goes into within the allowable value range and thereafter goes beyond the allowable value range again, and then adds the subject cases to a cluster that is newly formed. For example, in an instance shown in Fig. 38, since the control quantities of the first and second cases exceed the allowable value range with respect to the target quality, "0" is stored for each of the two cases and, for the third case, "1" is stored because the control quantity thereof exists within the range of the allowable value. If the status changes after the third print and the control quantity goes beyond the allowable value with respect to the target quality in the fourth print, then "0" is stored and the change from "1" to "0" is detected by the non-convergence judging part 43. As a result of this, a new cluster is formed and the fourth control case is classified into the new cluster that is different from the cluster in which the above-mentioned first and second control cases are classified. In this case, if it is assumed that a control rule can be formed when three control cases are collected, then in an instance shown in Fig. 34, no control rule can be formed in any of the clusters. After then, when the control quantity exceeds the allowable value range, then it is assumed that the fourth control quantity occurs as the result that the status surrounding the then printer has changed, and thus a control rule will be formed on this assumption. On the other hand, when the control quantity does go beyond the allowable value range, then it is judged that the fourth control quantity is caused not by the change of the status surrounding the then printer but occurs simply because the control quantity happens to exceed the allowable value range temporarily due to noise or the like, a new control rule will not be formed. That is, in the latter instance, in the fourth and its following controls, operations may be performed in accordance with the control rule that has been used. As described above, in the second embodiment, since a new cluster is not formed until the number of prints or the working time reaches a preset value, even if the status changes within this period and thus the control quantity exceeds the target quality to thereby be able to obtain a new control case, control cases to be obtained thereafter are classified into the same previous cluster. Therefore, the second embodiment is not able to cope with the constantly changing status. Also, when the factors that provide the control cases such as the control quantity and operation quantity (especially, the control quantity) provide an abnormal value temporarily due to noise or the like, or when the control quantities do not exceed the allowable value range continuously (mostly, due to noise), according to the second embodiment, the then control cases are classified into the same cluster as the previous control cases, so that the control rule is updated with the noise contained therein (the noise component can be weakened statistically according to the number of control cases but, if the number of control cases is small, then the noise component can raise a serious error). Or, even in the case where the noise component is not so great as to cause the control rule to be changed, and even when the control quantity converges into the allowable value once and thereafter goes again beyond the allowable value, the existing control rule is forcedly updated in spite of the fact that it is suitable for the current status. On the other hand, according to the present embodiment, when the control quantity converges once, that is, when the control quantity goes into within the allowable value range once, by classifying a control case next exceeding the allowable value range into a newly formed cluster, a control rule capable of coping with the current status transition more suitably can be formed. Also, when the status is caused to change temporarily due to noise or the like, an abnormal control case caused by the noise is not added into the cluster (the newest cluster) in which the control cases obtained so far have been classified, thereby being able to prevent the accuracy of the control rule from being lowered. (2) Operation Next, description will be given below of the operation of the present embodiment having the above-mentioned structure. In the following description, the operation of the present embodiment will be discussed by comparison with that of the second embodiment. (1) Status Change Due to Environmental Change Fig. 39 is a conceptual view showing a method for taking a control case according to the second embodiment, while Fig. 40 is a conceptual view showing a method for taking a control case according to the present embodiment. Firstly, in the second embodiment, as shown in Fig. 39, a first cluster is formed for the first three prints and three control cases have been obtained until the third print. Therefore, a control rule is formed in accordance with the first cluster. In the 4th to 7th prints, the comparison result between the control quantity and the target quality does not exceed the allowable value range and, therefore, no cluster can be formed. Next, if, just after the 7th print is completed, the environmental condition and the like changes and the status changes at the 8th print, of course, the operation quantity cannot be corrected accurately by the existing control rule and thus the comparison result between the control quantity and the target quality goes beyond the allowable value range. The status quantity comparator 26 compares the time information on the control case in the newest cluster containing therein a group of control cases with the time information on a control case which is newly written into the control case memory 25 and, if a time difference between them is within a preset time, then the comparator 26 judges that the two control cases are similar in status to each other and, if the time difference is greater than the preset time, then the comparator 26 judges that the two control cases are not similar in status to each other. In this case, if time has not elapsed so long to the 8th print since the third print, then the above-mentioned judgment result shows that the two control cases are similar in status to each other and thus the control case of the 8th print, as shown in Fig. 39, is classified into the first cluster that was formed in the first to third prints. Similarly, in the 9th and 10th prints as well, the comparison result between the control quantity and the target quality exceeds the allowable value range and, therefore, the control cases of these prints are classified into the first cluster. In this manner, in the second embodiment, even if the status such as the environment and the like changes, the control cases that occur after then are classified into the newest cluster and, therefore, a control rule that corresponds to the prints (the 8th and its following prints) formed after the change of the status will not be formed. On the other hand, in the present embodiment, not only when a control quantity goes beyond a preset allowable value range, but also when the control quantity of a certain (a previous) print goes beyond the allowable value range and the control quantity of the current print goes within the allowable value range, the control cases are obtained. In Fig. 40, the non-convergence judging part 43 stores "0" for the first to third prints because the control quantities thereof respectively go beyond the allowable value range. For the 1st to 3rd prints, the status quantity comparator 26 obtains the control cases thereof and classifies them as a cluster because the control quantities thereof respectively exceed the allowable value range, similarly to the second embodiment. At a time point when three control cases are obtained, a control rule is extracted. Therefore, in all of the second to fourth embodiments, in the first to third prints, a first cluster is formed and three control cases are obtained until the third print, so that a control rule is formed in accordance with the first cluster. For the 4th print, since the control quantity thereof converges into within the allowable value range, the non-convergence judging part 43 stores "1". For the 5th to 7th prints, the judging part 43 similarly stores "1". Next, as described before, if the status such as the environment and the like changes just after the 7th print and the status changes in the 8th print, of course, the correction of the operation quantity cannot be made accurately by the existing control rule, so that the comparison result between the control quantity and the target quality goes beyond the allowable value range. In response to the fact that the comparison result goes beyond the allowable value range, the non-convergence judging part 43 stores "0" and , at the same time, the judging part 43 detects the change from "1" to "0" and thus supplies a non-detection signal to the status quantity comparator 26. On the other hand, the status quantity comparator 26 forms a second cluster different from the first cluster and classifies the control quantity of the 8th print into the second cluster. Similarly, in the 9th and 10th prints as well, since a control rule most suitable for the status thereof cannot be formed but the existing control rule is used, the control quantity thereof cannot be converged sufficiently but goes beyond the allowable value range. Therefore, the control cases in the 9th and 10th prints are classified sequentially into the second cluster. When three control cases are collected, then a new control rule is formed in accordance with the second cluster. As a result of this, since the control rule formed in the second cluster can be used effectively for the prints to be formed after then, the comparison result between the control quantity and the target quality converges into within the allowable value range. In this manner, in the present embodiment, if the current control quantity goes beyond the allowable value and the previous control quantity converges into within the allowable value range, then a control case exceeding the allowable value range is classified into another cluster which is newly formed, which makes it possible to form a control rule being able to cope with the current status transition more properly. (3) Temporary Status Change Fig. 41 is an conceptual view of a method for obtaining a control case according to the second embodiment, while Fig. 42 is an conceptual view of a method for obtaining a control case according to the present embodiment. First, in Fig. 41, from the first print to the seventh print, similarly to Fig. 39, a first cluster is formed in the first to third prints and a control rule is formed in accordance with the first cluster. In the fourth to seventh prints, the comparison result between the control quantity and the target quality does not go beyond the allowable value range and, therefore, no cluster is formed. Next, if the 8th print exceeds the allowable value range due to noise or the like, then the status quantity comparator 26 compares the time information on the control case within the newest cluster consisting of a group of control cases with the time information on a control case newly written into the control case memory 25 and, if a difference between the two times is within a given time, then the comparator 26 judges that the two control cases are similar in status to each other. On the other hand, if the time difference is greater than the given time, then the comparator 26 judges that the two control cases are not similar in status to each other. In this case, if time has not elapsed so long from the third print to the eighth print, then the judgment result shows that they are similar in status to each other and thus the control case of the eighth print, as shown in Fig. 41, is classified into the first cluster that was formed in the first to third prints. In the 9th print, the operation quantity is computed according to the control rule formed in the first cluster or other rules that have been stored so far. However, since the control quantity cannot be converged sufficiently but goes beyond the allowable value range due to the noise, the control case of the 9th print is classified into the first cluster. However, although the control quantity of the 9th print is caused to exceed the allowable value range by an abnormal cause such as the noise or the like, the control case of the 10th print is converged into the allowable value range. In the 11th print as well, if the control quantity is caused to go beyond the allowable value range due to an abnormal cause such as noise or the like, then the control case of the 11th print is also classified into the first cluster. In this manner, in the second embodiment, even if an abnormal control quantity is caused to occur temporarily due to the noise or the like, the subject control case is classified into the first cluster, with the result the that an error is contained in the subject control case. On the other hand, according to the present embodiment, during the first to seventh prints, similarly to Fig. 40, a first cluster is formed in the first to third prints and a control rule is formed in accordance with the first cluster. In the 4th to 7th prints, since the comparison result between the control quantity and the target quality does not exceed the allowable value range, no cluster is formed. Also, as shown in Fig. 42, the non-convergence judging part 43 stores "0" for the 1st to third prints because they exceed a preset allowable value, while it stores "1" for the 4th to 7th prints because they are converged into within the allowable value range. Next, as described above, if the 8th print is caused to go beyond the allowable value range due to noise or the like, then the non-convergence judging part 43 stores "0" and, at the same time, the part 43 detects the change from "1" to "0" and thus supplies a non-detection signal to the status quantity comparator 26. On receiving the non-detection signal, the status quantity comparator 26 creates a second cluster different from the first cluster and classifies the control quantity of the 8th print into the second cluster. In the 9th print, the operation quantity is computed according to the control rule formed in the first cluster or other control rules that have been stored so far. However, the effect of the noise still remains and cannot be controlled, so that the 9th print is caused to go beyond the allowable value range. Therefore, the non-convergence judging part 43 stores "0". Next, in the 10th print, as mentioned above, since it is converged into within the allowable value range, the non-convergence judging part 43 stores "1". When a control quantity is caused to go beyond the allowable value range again due to noise or the like in the 11th print, the non-convergence judging part 43 stores "0" and, at the same time, it detects the change from "1" to "2" and thus supplies a non-detection signal to the status quantity comparator 26. On receiving the non-detection signal, the status quantity comparator 26 newly forms a third cluster which is different from the second cluster and classifies the control quantity of the 11th print into the third cluster. Because the second and third clusters are formed due to noise or the like, there are contained a small number of control rules (less than three control cases) in the second and third clusters and, therefore, no control rule is formed in the second and third clusters. In this manner, according to the present embodiment, even if there occur control quantities which are caused to go beyond the allowable value range temporarily, they are not classified into the cluster (the first cluster) that was formed due to the status transition, which prevents abnormal control cases from being added to a normal cluster. Also, since a control rule is not calculated from a cluster which contains therein an abnormal control case caused by noise or the like, it is possible to secure a more accurate control rule which is free from the influence of a control case including noise. (3) Status Change Due to Environmental Change Now, Fig. 43 is a conceptual view of a method for obtaining a control case according to the second embodiment, while Fig. 44 is a conceptual view of a method for obtaining a control case according to the present embodiment. In Figs. 43 and 44, there is shown a case where the changes in the control quantities for the respective numbers of prints are the same but the status such as the environment or the like has changed. Firstly, in the second embodiment, as shown in Fig. 43, just after the 7th print, the status has been changed due to the environment or the like and the control quantities of the 8th, 9th and 11th prints are classified into the first cluster that was formed in the 1st to third prints. Therefore, as described above, the control rule formed in accordance with the first cluster is changed by the control case of the 8th, 9th and 11th prints. On the other hand, in the present embodiment, as shown in Fig. 44, the convergence judgment by the non-convergence judging part 43 varies from "1" to "0" between the 7th and 8th prints and, therefore, a second cluster is newly formed at this time and the control quantity of the 8th print is classified into the second cluster. Next, the control quantity of the 9th print is classified into the second cluster because it goes beyond the allowable value range. At this time, the non-convergence judging part 43 stores "0". Since the control quantity of the 10th print goes into within the allowable value range, the non-convergence judging part 43 stores "1". Further, as the control quantity of the 11th print goes beyond the allowable value range again, the non-convergence judging part 43 stores "0". Because the convergence judgment by the non-convergence judging part 43 varies from "1" to "0" between the 10th and 11th prints, a third cluster is newly formed at this time and the control quantity of the 11th print is classified into the third cluster. In the second and third clusters, no new control rule is formed because the number of control cases obtained there is two or one. As described above, in the present embodiment, when compared with the second embodiment, when the status has been changed due to the environment or the like, even if there occur control cases which go beyond the allowable value range after the status transition, such control cases are not classified into the first cluster that was formed prior to the change of the status, thereby being able to prevent such abnormal control cases from being added to a normal cluster. The configuration and control procedures of the present, i.e., fourth embodiment are as described above. Now, a description will be given below of the actions and advantages of the fourth embodiment. (5) Advantages of Embodiment 4 (a) When the control quantity exceeds the allowable value range with respect to the target quality, if the previous control quantity converges, then a new cluster is formed and the control quantity exceeding the allowable value range is classified into the new cluster to thereby obtain a sufficient number (3) control cases, which makes it possible to form a control rule capable of coping with the current status transition more properly. (b) Also, even if there occur control quantities exceeding the allowable value range temporarily due to noise or the like, these abnormal control cases are prevented from being added to a normal cluster in which control cases obtained and formed so far and consisting of accurate control quantities are classified. (c) Further, since only the obviously abnormal control cases caused by noise or the like are classified into a new cluster and also since (unexpected or sudden) noise is not considered to occur continuously after then, a sufficient number of (for example, 3) control cases to form a control rule will-not be classified into the new cluster. Therefore, the cluster that is newly formed here will not be stored. As a result of this, a control rule is formed only from the clusters that have been stored so far, which makes it possible to obtain a more accurate control rule free from the effects of noise, so that a control processing can be performed in accordance with the control rule which is able to cope with the current status change more properly. As described above, there are two kinds of status transitions. In one status transition, the measured density value changes from within an allowable density value range to outside the allowable density value range. In the another status transition, the measured density value changes from outside the allowable density value range into within the allowable density value range. These status transitions may be detected in the following manner. That is, the density comparator 24 judges, by means of "1" and "0" flags, whether the measured density value is within or outside the allowable density value range, and outputs the judgment result to the convergence/non-convergence judging part 43. The convergence/non-convergence judging part 43 includes two flag check memories respectively for storing the judgments as to whether the output of the developed density sensor 10 is within or outside the allowable density value range defined by the density comparator 24. One flag check memory is used to store the judgment result on the density value measured at the current time, while the other is used to store the judgment result on the density value measured just before the currently measured density value. The two flag check memories are respectively set for 0 in their initial states. The convergence/non-convergence judging part 43 allows one flag check memory to store the judgment result on the currently measured density value supplied from the density comparator 24, and judges a case relating to the currently measured density value. Then, the judging part 43 allows one flag check memory to store the judgment result on the density value measured just before the currently measured density value and also allows the other flag check memory to store the judgment result that has been stored in one flag check memory. In this state, the convergence/non-convergence judging part 43 judges a case relating to the newest measured density value. The convergence/non-convergence judging part 43 can judge the status transition of the density value to be detected by using the two flag check memories. As will be described later, the status transition, in which the measured density value changes from within the allowable density value range to outside the allowable density value range, can be judged by finding a state in which the judgment results stored in the two flag check memories are respectively 0 and 1. On the other hand, the status transition, in which the measured density value changes from outside the allowable density value range into within the allowable density value range, can be judged by finding a state in which the judgment results stored in the two flag check memories are respectively 1 and 0. For the status transition from within the allowable density value range to outside the allowable density value range as well as for the status transition from outside the allowable density value range into within the allowable density value range, the convergence/non-convergence judging part 43 transmits these measured density values to the control case memory part 25. The control case memory part 25 combines the measured density values with operation quantities supplied from the operation quantity memory 32 and corresponding to the measured density values and status quantities supplied from the timer 40 and corresponding to the measured density values, thereby obtaining cases. Therefore, a control rule is formed from the thus obtained cases. When the judgment results stored in the two flag check memories are both 0, then the measured density values are transmitted from the convergence/non-convergence judging part 43 to the control case memory part 25, where the measured density values are combined with the operation quantities and status quantities obtained in the control case memory part 25 to thereby obtain cases. The thus obtained cases are classified into a cluster within the status quantity comparator 26 and are used to form a control rule similarly to the above-mentioned case. As has been described heretofore, according to the invention, due to the fact that the transition of the status is judged and, if the status transition is found, then a new control rule is formed from new control cases, there is eliminated the need to build a complicated control system previously on the assumption of various environmental conditions. Also, since the transition of the status and the formation of the control rule are carried out in an ex post facto manner, when image forming apparatuses are different from one another in performance and history, such differences can be adapted. The present invention is able to cope automatically with a change in the performance of an image forming apparatus caused by parts replacement or by aging. Further, according to the invention, there are used a plurality of control rules, the control rules are combined together with a weight corresponding to the adaptability to thereby form an application control rule, and a control processing is carried out according to the application control rule, so that, even if each of the control rules is only able to perform a rough control processing, a fine control processing can be achieved finally.

Claims: An image forming apparatus for controlling an operation quantity so that a control quantity relating to image quality becomes a target value, said image forming apparatus comprising: means for setting said target value; means for measuring said control quantity; memory means for storing as control rules a plurality of functions each of which defines, in a coordinate space of said operation quantity and said control quantity, a surface representative of coordinates of operation quantities actually applied in a series of image forming processes and corresponding control quantities actually measured; application control rule generating means for generating an application control rule to be used to calculate said operation quantity based on said control quantity from said one function or by combining at least part of said plurality of functions; means for varying said operation quantity in accordance with said application control rule and an error between said control quantity and said target value; status transition judging means for judging whether a transition of a status of a main body of said image forming apparatus has occurred; and means for determining a new function from operation quantities actually applied and control quantities actually measured in a series of image forming processes in response to a judgment output of said status transition judging means, and for causing said memory means to store said new function as a new control rule, wherein said application control rule generating means generates a new application control rule with said new control rule taken into account. The image forming apparatus as set forth in Claim 1, wherein said status transition judging means detects occurrence of an event may cause, at a high probability, a transition of said status of said image forming apparatus main body, and judges that said transition of said status of said image forming apparatus main body has occurred, in response to detection of said event. The image forming apparatus as set forth in Claim 2, wherein said event includes one or both of a fact that a working time of said image forming apparatus main body has exceeded a given time after a previous affirmative judgment of a transition of said status and a fact that the number of image prints formed by said image forming apparatus main body has exceeded a given number after said previous affirmative judgment of said transition of said status. The image forming apparatus as set forth in Claim 1, further comprising: allowable range setting means for setting an allowable range of said error; and judging means for judging whether said error is out of said allowable range, wherein said measured values of said actually applied operation quantities and said actually measured control quantities that are used to determine said new function in response to said judgment outputs are those obtained in a series of image forming processes performed after said judgment output and satisfying a condition that said error is out of said allowable range. The image forming apparatus as set forth in Claim 1, further comprising: means for determining, for each of said control rules, a difference between a measured control quantity that is measured when an operation quantity is actually applied and an expected control quantity that is obtained when said operation quantity is applied to said control rule; and application control rule generating means for generating an application control rule to be used to calculate said operation quantity based on said control quantity by combining at least part of said plurality of control rules with weighting in which a weight increases as said difference decreases. The image forming apparatus as set forth in Claim 1, further comprising: means for determining adaptabilities of said respective control rules with respect to an actually applied operation quantity and an actually measured control quantity in one or a plurality of control operations actually carried out. The image forming apparatus as set forth in Claim 5, wherein said surface is a plat plane. The image forming apparatus as set forth in Claim 5, wherein said surface is a curved surface. The image forming apparatus as set forth in Claim 5, wherein said application control rule generating means selects, in accordance with said differences, said at least part of said plurality of control rules used to generate said application control rule. The image forming apparatus as set forth in Claim 5 or 6, wherein at least part of said plurality of control rules correspond respectively to typical statuses of said image forming apparatus main body. The image forming apparatus as set forth in Claim 9, further comprising: means for causing said memory means to store said new control rule in place of a control rule that has not been selected for a longest time by said selecting means. The image forming apparatus as set forth in Claim 1, 5 or 6, wherein said image forming apparatus main body is so configured as to charge a surface of a photoconductive material uniformly, then form a latent image on said photoconductive material surface by use of a light illumination device, and then develop said latent image with a toner material, and wherein said control quantity relating to said image quality is optical densities of two kinds of developed patches formed on said photoconductive material surface, and said operation quantity is an output of said light illumination device and a potential of said uniform charging. The image forming apparatus as set forth in Claim 4, further comprising convergence judging means for detecting, based on a judgment result of said judging means, a converging case- with which said error changes from out of said allowable range to within it, wherein said measured values of said actually applied operation quantities and said actually measured control quantities that are used to determine said new function include said converging case detected by said convergence judging means. The image forming apparatus as set forth in Claim 13, wherein said allowable range is a visually allowable error range. The image forming apparatus as set forth in Claim 13, further comprising case storing means for storing converging cases detected by said convergence judging means and cases that are judged to be out of said allowable range by said judging means, wherein said new function determining means determines said new function based on a given number of cases stored in said case storing means. The image forming apparatus as set forth in Claim 15, wherein said given number of said cases that are necessary for said new function determining means to determine said new function is 3 or more. The image forming apparatus as set forth in Claim 15, further comprising counting means for counting the number of cases stored in said case storing means, wherein said new function determining means determines said new function based on said given number of cases stored in said case storing means in accordance with a count supplied from said counting means. The image forming apparatus as set forth in Claim 4, further comprising non-convergence judging means for detecting, based on a judgment result of said judging means, a non-converging case with which said error changes from within said allowable range to out of it, wherein said measured values of said actually applied operation quantities and said actually measured control quantities that are used to determine said new function include said non-converging case detected by said non-convergence judging means. The image forming apparatus as set forth in Claim 18, wherein said allowable range is a visually allowable error range. The image forming apparatus as set forth in Claim 18, further comprising case storing means for storing non-converging cases detected by said non-convergence judging means and cases that are judged to be out of said allowable range by said judging means, wherein said new function determining means determines said new function based on a given number of cases stored in said case storing means. The image forming apparatus as set forth in Claim 20, wherein said given number of said cases that are necessary for said new function determining means to determine said new function is 3 or more. The image forming apparatus as set forth in Claim 20, further comprising counting means for counting the number of cases stored in said case storing means, wherein said new function determining means determines said new function based on said given number of cases stored in said case storing means in accordance with a count supplied from said counting means. The image forming apparatus as set forth in Claim 1, further comprising wherein said application control rule generating means generates a new application control rule from only said new control rule or by combining a plurality of control rules stored in said memory means including said new control rule. The image forming apparatus as set forth in Claim 1, further comprising control rule deviation calculating means for catculating, for each of said control rules stored in said memory means, a deviation between a measured control quantity measured by said control quantity measuring means and an expected control quantity that is expected when said measured control quantity is applied to said control rule. A method for controlling an image forming apparatus which controls an operation quantity so that a control quantity relating to image quality becomes a target value, said method comprising the steps of: setting said target value; measuring said control quantity; storing as control rules a plurality of functions each of which defines, in a coordinate space of said operation quantity and said control quantity, a surface representative of coordinates of operation quantities actually applied in a series of image forming processes and corresponding control quantities actually measured; determining, for each of said control rules, a difference between a measured control quantity that is measured when an operation quantity is actually applied and an expected control quantity that is obtained when said operation quantity is applied to said control rule; generating an application control rule to be used to calculate said operation quantity based on said control quantity by combining at least part of said plurality of control rules with weighting in which a weight increases as said difference decreases; and varying said operation quantity in accordance with said application control rule and an error between said control quantity and said target value. The method as set forth in Claim 25, further comprising the steps of: generating one or a plurality of control rules of an initial state based on operation quantities actually applied and corresponding control quantities actually measured; judging whether a transition of a status of a main body of said image forming apparatus has occurred; generating a new control rule based on operation quantities and control quantities obtained in an image forming processes in response to an affirmative judgment of a transition of said status; and varying said application control rule with said new control rule taken into account. The method as set forth in Claim 25, further comprising the step of: determining adaptabilities of said respective control rules with respect to an actually applied operation quantity and an actually measured control quantity in one or a plurality of control operations actually carried out.

Inventors: ISHIKAWA KIYOTAKA, YAMADA KUNIO, ISHIKAWA, KIYOTAKA, YAMADA, KUNIO, Ishikawa, Kiyotaka, c/o Fuji Xerox Co., Ltd., Yamada, Kunio, c/o Fuji Xerox Co., Ltd.
Applicants: FUJI XEROX CO LTD, FUJI XEROX CO., LTD.
Application year: 1996
Main classification: H04N1
IPC codes: G05B13, H04N1, G03G15, B41B27