Automatic adjusting apparatus for equipment

Abstract: An automatic adjusting apparatus for adjusting equipment which comprises an adjustment state detecting section and a processing section. The detecting section (2,3) detects an adjustment state of the equipment so as to output one or a plurality of adjustment state indexes on the basis of the detected adjustment state and the processing section (10) performs the process on the basis of the adjustment state index to adjust the equipment. The processing section (10) is equipped with an adjustment state evaluation section (11) for calculating and outputting, on the basis of the adjustment state index, an adjustment evaluation value and an adjustment amount calculation section (12) for calculating, on the basis of the adjustment state index, adjustment amounts for one or a plurality of portions of the equipment. Also included in the processing section is an adjustment control section (13) for selecting, on the basis of the adjustment evaluation value, an adjustment amount from the outputs of the calculation section. The control section (13) controls the calculation section (12) so as to control the adustment amount on the basis of the selection result. The calculation section (12) has a function to learn the relation between the adjustment state index and the adjustment amount and a function to calculate the adjustment amount in accordance with the learned relation.

Description: BACKGROUND OF THE INVENTION The present invention relates to an automatic adjusting apparatus for adjusting equipment at the time of manufacturing or using so that the equipment can normally operate. Conventionally, the adjustment of a plurality of portions of equipment correlative with each other is principally effected by a person skilled in the adjusting work, while the adjustment is partially and automatically effected. However, the automatic adjusting system is arranged so as to effect the adjustment with the respective adjusting portions being successively and slightly moved or under the condition of being specialized for each equipment. For instance, in manufacturing a television picture tube, the white balance adjusting process for balancing between three colors of red, green and blue is made as follows. That is, a test pattern generator (not shown) displays a bright white (bright portion 51) and a dark gray (dark portion 52) on a cathode ray tube of the television picture tube, as illustrated in Fig. 3, so as to measure the brightnesses of the three colors of red, green and blue at the bright portion 51 and the dark portion 52 by means of a sensor so that a skilled worker adjusts four adjusting volumes to cause the values to be coincident with set values. Moreover, a person successively and slightly operates an adjusting volume by a value set by a program through a device with the brightness of the screen being measured by means of a sensor, thereby automatically performing the adjustment. There is a problem which arises with such a white balance adjustment for the television picture tube, however, in that the worker is required to be highly skilled because the plurality of adjusting portions correlative with each other and the respective adjusting portions greatly have non-linearities. Further, the method of slightly operating the respective volumes through the device has disadvantages that a long time is taken until the adjustment completion and an actually usable adjusting system is required to be controlled in accordance with a complex program made on the basis of the actual adjusting experience. In EP-A-0 204 112 a system for automatically adjusting the operating circuits of a television set or monitor to reduce image distortion to an acceptable level is described. The system comprises a computer for generating a sequence of images for application to the video equipment being adjusted, a scanner for viewing selected portions of the video image on the set under control of signals from the computer and providing signals specifying the image viewed to the computer. The computer generates basing on stored data signals for manipulating a robotic means for adjusting one or more circuit components. If the measured parameters are within determined tolerance limits adjustment of the next image parameter is executed independently from the just finished adjustment. After a certain number of unsuccessfully attempted adjustments a fail status message is sent to the central processor and the test and adjustment cycle is terminated. The article "Adaptive (neural network) Control in Computer-Integrated-Manufacturing" published in "Proceedings of SPIE - The International Society for Optical Engineering", volume 937, "Applications for Artificial Intelligence VI", 4-6 April 1988, discloses in general principles of adaptive control in CIM (computer-integrate-manufacturing). The adaptive control problem in CIM is defined by the following ingredients: 1) a collection of analog and digital inputs; some are sensory inputs, some are initiation inputs, and some human operator inputs. 2) a collection of digital outputs; some are actuations of robots, programmable controllers and other field devices, some are diagnosis signals, and some are measurement readings. 3) a collection of hidden units, their sigma-pi units and links; these units and links describe the structural behaviour of the system, and there state and link values describe the information flow of the CIM system. In IEEE ASSP magazine, April 1987, pages 4 to 22 "An Introduction to Computing with Neural Nets" is published. This publication discloses improving the performance of a neural network algorithm by making many training runs starting with different sets of random weights. The problem of local minima can be minimized by using multiple starts with difference random weights and a low gain to adapt weights. SUMMARY OF THE INVENTION The present invention has been developed in order to solve the above-described problems and contemplates to automatically and effectively perform a complex adjusting work such as the white balance adjustment of the television picture tube. In accordance with the present invention, there is provided an automatic adjusting apparatus comprising: (a) adjustment state detecting means for detecting adjustment states of equipment in process of being adjusted, for generating adjustment state indexes representing the adjustment states based on the detected adjustment states; (b) adjustment state evaluation means for calculating adjustment evaluation values representing levels of the adjustment states based on the adjustment state indexes; (c) adjustment amount calculation means for learning relations between the adjustment state indexes and the adjustment amounts, the adjustment amounts representing amounts of adjusting the equipment, and for calculating the adjustment amounts according to the learned relations; (d) adjustment control means for comparing the adjustment evaluation values, for selecting an adjustment amount to improve current adjustment states based on the result of comparing in order to reach an adjustment amount suitable for a predetermined adjustment state; and (e) adjustment output means for adjusting the equipment according to the selected adjustment amount, characterized in that (f) the adjustment control means generates control signals representing whether or not the current adjustment states are improved based on the result of comparing, (g) the adjustment amount calculation means randomly generates an adjustment amount in receiving the control signal representing one of (i) that the adjustment amounts calculated based on the learned relations can not improve the current adjustment states, and (ii) that the adjustment amounts which has been randomly generated before can not improve the current adjustment states, and (h) the output means adjusts the equipment according to the adjustment amount newly generated. Preferably, the adjustment amount calculation means includes neural network means having the learning function, random number generating means for randomly generating the adjustment amounts and learning control means for controlling the neural network means and the random number generating means in accordance with a control signal from the adjustment control means. BRIEF DESCRIPTION OF THE DRAWINGS The object and features of the present invention will become more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings in which: Fig. 1 is a block diagram showing an arrangement of an automatic adjusting apparatus according to an embodiment of the present invention; Fig. 2 is a block diagram showing an arrangement of an adjustment amount calculation means to be used in the Fig. 1 automatic adjusting apparatus; Fig. 3 is an illustration of a pattern on a screen of a television picture tube; and Fig. 4 is a flow chart showing the operation to be executed by the Fig. 1 automatic adjusting apparatus. DETAILED DESCRIPTION OF THE INVENTION Referring nore to Fig. 1, there is illustrated an automatic adjusting apparatus according to an embodiment of the present invention. Here, the automatic adjusting apparatus of this embodiment is employed for the white balance adjustment of a television picture tube. In Fig. 1, illustrated at numeral 1 is an adjustment state detecting means which is composed of RGB sensors 2, 3, an aplifier 6 and an analog-to-digital (A/D) converter 7. The RGB sensor 2 is arranged to measure the brightnesses of red (R), green (G) and blue (B) of a bright portion 51 of a pattern as illustrated in Fig. 3 which is displayed on a cathode ray tube of the television picture tube to be adjusted, thereby outputting a luminance signal 4 to the amplifier 6. Further, the RGB sensor 3 is similarly arranged to measure the brightnesses of R, G and B of a dark portion 52 of the pattern as illustrated in Fig. 3 so as to output a luminance signal 5 to the amplifier 6. The amplifier 6 amplifies the respective luminance signals 4 and 5 so as to output a luminance signal 7 to the A/D converter 8. The A/D converter 8 converts the luminance signal 7 into a digital form so as to output as an adjustment state index 9 the digital luminance values of the red, green and blue of the bright portion 51 and dark portion 52 to an adjustment state evaluation means 11 and further to an adjustment amount calculation means 12. The adjustment state evaluation means 11, the adjustment amount calculation means 12 and an adjustment control means 13 (which will hereinafter be described in detail) can be realized by a program to be executed by a microcomputer illustrated at numeral 10. Here, let it be assumed that the luminance values of R, G and B of the bright portion 51 and dark portion 52 of the adjustment state index 9 are taken as Ii (i = 1 to 6) and the reference luminance values of R, G and B of the bright portion 51 and dark portion 52 which are set as reference values for white color are taken as Di (i = 1 to 6). The adjustment state evaluation means 11 calculates each of (Di - Ii) 2 and further calculates the total sum of the calculation results which is in turn outputted as an adjustment evaluation value 14 to the adjustment control means 13. The adjustment evaluation value 14 is always a positive value and the adjustment state is more desirable as the adjustment evaluation value 14 is smaller. Here, an adjustment evaluation value is predetermined whereby the work for the adjustment is completed when the adjustment evaluation value 14 becomes below the predetermined adjustement evaluation value. The adjustment amount calculation means 12 learns the relation between the adjustment state index 9 and the adjustment amount in accordance with a control signal from the adjustment control means 13 so as to calculate an adjustment amount 15 on the basis of the adjustment state index 9 in accordance with the relation. Furthermore, if the value of the adjustment amount 15 based on the learning is inadequate, the adjustment amount calculation means 12 randomly generates an adjustment amount 15 in accordance with the control signal 16 from the adjustment control means 13. Here, the adjustment amount 15 takes four real vectors corresponding to four adjusting volumes of the television picture tube, and the adjustment amount 15 to be randomly generated is normalized so that the absolute value of the vector becomes constant. Secondly a description will be made hereinbelow with reference to Fig. 2 in terms of the adjustment amount calculation means 12. In Fig. 2, a neural network means 31 is responsive to a control signal 34 from a learning control means 32, which includes the adjustment state index 9, so as to calculate an adjustment amount 35 with respect to the adjustment state index 9 to output the calculated adjustment amount 35 to the learning control means 32. In addition, the neural network means 31 learns the relation between the adjustment state index 9 and the adjustment amount corresponding to the adjustment state index 9. Here, the neural network means 31 can comprise a combination of an error back-propagation learning and a three-layer perceptron where the input layer whose units are 6 adjustment indexes 9, the output layer whose units are 4 adjusting portions and the imbedded layer is one layer whose unit number is 8. The hierarchical type perceptron and the error back-propagation learning are disclosed in detail in "Neural Network Information Process" 1988 written by Hideki Aso and published by Sangyo Tosho Co., Ltd., for example. In response to a control signal 37 from the learning control means 32, a random number generator 33 randomly generates the adjustment amount 36 which is in turn supplied to the learning control means 32. Each of the adjustment amounts 35 and 36 is a four-dimensional vector comprising four real numbers. The learning control means 32 is responsive to the adjustment state index 9 from the adjustment state detecting means 1 so as to causes the neural network means 31 or the random number generator 36 to calculate the adjustment amount at that time, the calculated adjustment amount being inputted as the adjustment amount 35 or 36 therefrom to the learning control means 32. Further, the absolute value (norm) of the adjustment amount (vector amount) is normalized to become constant so as to be outputted as the adjustment amount 15. Still further, in accordance with the control signal 16, the neural network means 31 learns the relation between the adjustment state index 9 and the adjustment amount 15. Returning back to Fig. 1, the adjustment control means 13 selects the adjustment amount 15 of the adjustment amount calculation means 12 in accordance with the adjustment evaluation value 14 so that the adjustment evaluation value 14 becomes smaller. That is, the control signal 16 is outputted to the adjustment amount calculation means 12 and an adjustment amount 17 is outputted to an adjustment output means 27 so that the adjustment amount for causing the adjustment evaluation value 14 to become smaller is used for the adjustment. The adjustment output means 27 is composed of a motor driver 18 and step motors 19 to 22. The motor driver 18 receives the adjustment amounts 17 of the adjusting volumes for four adjusting portions of the television picture tube from the adjustment control means 13 and outputs pulses 23 to 26 to the step motors 19 to 22 corresponding to the respective adjusting portions so that the step motors 19 to 22 are rotated by angles respectively corresponding to the adjustment amounts 17. The rotating shafts (not shown) of the step motors 19 to 22 respectively have driver-like configurations which can be inserted into the adjusting volumes (not shown) for the four adjusting portions of the television picture tube. The operation of the Fig. 1 automatic adjusting apparatus will be described hereinbelow with reference to a flow chart of Fig. 4. First, the screen of the television picture tube to be adjusted is arranged to display a pattern as illustrated in Fig. 3, whereupon the sensors 2 and 3 are arranged to be in confronting relation to the cathode ray tube of the television picture tube and the shafts of the step motors 19 to 22 are inserted into the adjusting volumes of the television picture tube. Thereafter, the white balance adjusting operation is executed as follows: 1) the sensors 2 and 3 respectively measure the brightnesses of R, G and B at the bright portion 51 and dark portion 52 (see Fig. 3) on the screen of the television picture tube and the measurement results are amplified by the amplifier 6 and then A/D-converted from the analog signals into digital signals so as to obtain an adjustment state index 9; 2) the adjustment state index 9 is inputted to the adjustment state evaluation means 11 so as to calculate an adjustment evaluation value 14 at that time, and the adjustment amount calculation means 12 calculates the adjustment amounts 35 for the four adjusting portions of the television picture tube on the basis of the adjustment evaluation value 14 using the neural network means 31 and further calculates the adjustment amount 15 to be outputted, and the adjustment control means 13 directly outputs to the adjustment output means 27 the adjustment amount 15 outputted from the adjustment amount caluclation means 12 so that adjustment output means 27 rotates the adjusting volumes for the four adjusting portions of the television picture tube; 3) after the adjustment, the adjustment state detecting means 1 measures the adjustment state index 9 from the screen of the television picture tube and the adjustment state evaluation means 11 further calculates the adjustment evaluation value 14 so that the present adjustment evaluation value 14 is compared with the previously measured adjustment evaluation value 14 and, if the present adjustment evaluation value 14 is smaller than the previous evaluation value 14, that is, when the adjustment state is directed to become better, the operation will advances to a step 7 which will be described hereinafter; 4) if the previously measured adjustment evaluation value 14 is smaller than the present adjustment evaluation value 14, that is, when the adjustment state is directed to become deteriorated, with the signs of the respective elements of the adjustment amount 17 previously outputted from the adjustment control means 13 to the adjustment output means 27 being inverted, the adjustment control means 13 again outputs the adjustment amount 17 so that the adjusting volumes for the four adjusting portions of the television picture tube are respectively rotated so as to cancel the previous adjustment; 5) the adjustment control means 13 outputs the control signal 16 to the adjustment amount calculation means 12 to cause the random number generator 33 to randomly generate the adjustment amounts 36 for the four adjusting portions of the television picture tube and cause the adjustment amount 15 to be calculated and then outputted from the adjustment amount calculation means 12, and further the adjustment 15 is outputted as the adjustment amount 17 from the adjustment control means 13 to the adjustment output means 27 so as to adjust the adjusting volumes for the four portions of the television picture tube; 6) The operational flow again proceeds to the above-described 3); and 7) the adjustment control means 13 outputs the control signal 16 to the adjustment amount calculation means 12 so that the neural network means 31 of the adjustment amount calculation means 12 learns the relation between the final adjustment amount and the adjustment state index 9. This adjustment operation is repeatedly performed until the adjustment evaluation value 14 becomes smaller than a predetermined value and completed when it becomes smaller than the predetermined value. If the above-described adjustment operation is repeatedly effected for a number of television picture tubes, the neural network means 31 of the adjustment amount calculation means 12 gradually learns the relation of the adjustment amount 35 with respect to the adjustment state index 9, thereby cancelling the requirement to randomly generate the adjustment amount 37 so as to automatically and effectively perform the white balance adjustment of the television picture tube. Although in the above-described embodiment the neural network means 31 is constructed by a multi-layer perceptron, it is appropriate to use a vector quantization learning or the like. Further, although the neural network means 31 is used as the learning means of the adjustment amount calculation means, it is appropriate to use a system such as a statistical process and a polynomial approximation. Still further, although one adjustment amount is learned with respect to one adjustment state index, it is also appropriate that a plurality of adjustment amounts are learned with respect to one adjustment index. In this case, the use of the adjustment amount of the random number generator is made after all the adjustment amounts learned with respect to one adjustment state index are used. Moreover, although the adjustment state evaluation means, adjustment amount calcualtion means and adjustment control means are constructed by a program to be executed by a microcomputer, it is also appropriate to effect them by using a special LSI. In addition, although the automatic adjusting apparatus is employed for the white balance adjustment of the television picture tube, it is possible to use it for the other adjustments. Further, although the automatic adjusting apparatus is used independently from the equipment to be adjusted, it is also appropriate to incorporate it into the equipment to be adjusted. According to the above-described embodiment, it is not required to use a skilled worker because the automatic adjusting apparatus automatically performs the adjustment. Further, since the adjustment amounts are randomly generated at the time of being non-learned and only a desrable adjustment amount is used, it is not required to designate the adjustment amount corresponding to the adjustment state through a program made by a person, thereby easily using the automatic adjusting apparatus for various kinds of adjustments. In addition, since the adjustment amounts are learned with respect to various adjusting states, it is possible to perform the adjustment so as to be suitable for the adjustment state, thereby shortening the adjusting time period to effectively effecting the adjustment.

Claims: An automatic adjusting apparatus comprising: (a) adjustment state detecting means (1) for detecting adjustment states of equipment in process of being adjusted, for generating adjustment state indexes (9) representing the adjustment states based on the detected adjustment states; (b) adjustment state evaluation means (11) for calculating adjustment evaluation values (14) representing levels of the adjustment states based on the adjustment state indexes (9); (c) adjustment amount calculation means (12) for learning relations between the adjustment state indexes (9) and the adjustment amounts (15), the adjustment amounts (15) representing amounts of adjusting the equipment, and for calculating the adjustment amounts (15) according to the learned relations; (d) adjustment control means (13) for comparing the adjustment evaluation values (14), for selecting an adjustment amount (15) to improve current adjustment states based on the result of comparing in order to reach an adjustment amount (15) suitable for a predetermined adjustment state ; and (e) adjustment output means (27) for adjusting the equipment according to the selected adjustment amount (15), characterized in that (f) the adjustment control means (13) generates control signals (16) representing whether or not the current adjustment states are improved based on the result of comparing, (g) the adjustment amount calculation means (12) randomly generates an adjustment amount (15) in receiving the control signal (16) representing one of (i) that the adjustment amounts (15) calculated based on the learned relations can not improve the current adjustment states, and (ii) that the adjustment amounts (15) which has been randomly generated before can not improve the current adjustment states, and (h) the output means (27) adjusts the equipment according to the adjustment amount (15) newly generated. An automatic adjusting apparatus as claimed in claim 1, wherein said adjustment amount calculation means (12) includes neural network means (31) having the learning function, random number generating means (33) for randomly generating the adjustment amounts (15) and learning control means (32) for controlling said neural network means (31) and said random number generating means (33) in accordance with a control signal (16) from said adjustment control means (13).

Inventors: KINDO TOSHIKI, NAKA MOTOHIKO, SAITOH MIE, SHIDA TAKEHIKO, TANAKA TAKEHISA, YOSHIDA KUNIO, KINDO, TOSHIKI, NAKA, MOTOHIKO, SAITOH, MIE, SHIDA, TAKEHIKO, TANAKA, TAKEHISA, YOSHIDA, KUNIO
Applicants: MATSUSHITA ELECTRIC IND CO LTD, MATSUSHITA ELECTRIC INDUSTRIAL CO., LTD.
Application year: 1991
Main classification: H04N9
IPC codes: H04N9, G05B13, H04N17