Patent Publication Number: US-2023133009-A1

Title: Learning Data Generation Device, Learning Device, Control Device, Learning Data Generation Method, Learning Method, Control Method, Learning Data Generation Program, Learning Program, and Control Program

Description:
TECHNICAL FIELD 
     The technique of the disclosure relates to a learning data generating device, a learning device, a control device, a learning data generating method, a learning method, a control method, a learning data generating program, a learning program, and a control program. 
     BACKGROUND ART 
     There is conventionally known a plant controlling system that, by using start-up data of the plant, can realize identification of the plant and adjustment of control parameters (see, for example, Patent Document 1: Japanese Patent Application Laid-Open (JP-A) No. 2010-113638). 
     The system supporting tool disclosed in JP-A No. 2010-113638 estimates model parameters of the plant on the basis of the start-up data and by using an identification function. Then, by using a control parameter adjusting function, the system supporting tool computes control parameters relating to a model prediction control section and a PID control section by using model parameters that are the results of identification. 
     SUMMARY OF INVENTION 
     Technical Problem 
     By the way, in any control system, there are cases in which observed data that has been observed from an object of control is set as learning data, and a model that operates in the control system is trained. In a case in which a decision tree model, which is a model expressing a decision tree, is trained as an example of such a model, the more the number of learning data increases, the more the number of layers of the decision tree model increases, and the more the computation time and the memory capacity of the model increase. 
     However, there is the need to reduce the computation time at the equipment that are incorporated in the control system. With regard to this point, in a case of training a decision tree model by using a huge amount of observed data that has been observed from an object of control, the computation time of the decision tree model and the memory capacity of the model increase, and therefore, it is not preferable to use a huge amount of learning data. 
     Thus, it is often the case that a small number of observed data are selected as learning data from the huge amount of observed data. However, in this case, if the observed data that are selected are not suitable as learning data, the decision tree model at which learning has been carried out by using this learning data also is not suitable. Specifically, in a case in which noise is included in the observed data that have been selected as the learning data, the accuracy of the decision tree model learned by using this learning data is low. However, it is difficult to sort out the learning data, which do not lower the accuracy of the decision tree model, from the huge amount of observed data. 
     In the technique disclosed in aforementioned JP-A No. 2010-113638, start-up data of a plant is collected, and model parameters are estimated on the basis of this start-up data, but sorting of the start-up data is not considered. Therefore, in a case of reducing the computation time of the decision tree model and the memory capacity of the model by using the technique disclosed in aforementioned JP-A No. 2010-113638, there is the problem that the start-up data must be sorted manually, and it is difficult to easily reduce the amount of learning data. 
     The technique of the disclosure was made in view of the above-described point, and an object thereof is to facilitate a reduction in the amount of learning data. 
     Solution to Problem 
     In order to achieve the above-described object, a learning data generating device relating to the technique of the disclosure is a learning data generating device comprising: an acquiring section acquiring a plurality of observed data that are observed data observed from an object of control and that express combinations of explanatory variables and objective variables; a teacher learning section that, on the basis of the plurality of observed data acquired by the acquiring section, trains a model for outputting the objective variable from the explanatory variable, and generates a learned teacher model; and a learning data generating section that, by inputting a predetermined explanatory variable to the teacher model generated by the teacher learning section, acquires a predetermined objective variable with respect to the predetermined explanatory variable, and generates a combination of the predetermined explanatory variable and the predetermined objective variable as learning data for training a decision tree model. 
     Further, the object of control can be a production device. 
     Further, a learning device relating to the technique of the disclosure is a learning device comprising: a learning data acquiring section that acquires the learning data generated by the above-described learning data generating device; and a learning section that trains the decision tree model on the basis of the learning data acquired by the learning data acquiring section. 
     Further, a control device relating to the technique of the disclosure is a control device comprising: an information acquiring section that acquires the explanatory variable from the object of control; and a control section that, by inputting the explanatory variable acquired by the information acquiring section to the decision tree model learned by the above-described learning device, acquires an objective variable corresponding to the explanatory variable, and carries out control corresponding to the objective variable on the object of control. 
     Further, a learning data generating method relating to the technique of the disclosure is a learning data generating method comprising: an acquiring section acquiring a plurality of observed data that are observed data observed from an object of control and that express combinations of explanatory variables and objective variables; on the basis of the plurality of observed data acquired by the acquiring section, a teacher learning section training a model for outputting the objective variable from the explanatory variable, and generating a learned teacher model; and a learning data generating section inputting a predetermined explanatory variable to the teacher model generated by the teacher learning section and thereby acquiring a predetermined objective variable with respect to the predetermined explanatory variable, and generating a combination of the predetermined explanatory variable and the predetermined objective variable as learning data for training a decision tree model. 
     Further, a learning method relating to the technique of the disclosure is learning method comprising: a learning data acquiring section acquiring the learning data generated by the above-described learning data generating method; and a learning section training the decision tree model on the basis of the learning data acquired by the learning data acquiring section. 
     Further, a control method relating to the technique of the disclosure is a control method comprising: an information acquiring section acquiring the explanatory variable from the object of control; and a control section inputting the explanatory variable acquired by the information acquiring section to the decision tree model learned by the above-described learning method and thereby acquiring an objective variable corresponding to the explanatory variable, and carrying out control corresponding to the objective variable on the object of control. 
     Further, a learning data generating program relating to the technique of the disclosure is a learning data generating program for causing a computer to function as: an acquiring section acquiring a plurality of observed data that are observed data observed from an object of control and that express combinations of explanatory variables and objective variables; a teacher learning section that, on the basis of the plurality of observed data acquired by the acquiring section, trains a model for outputting the objective variable from the explanatory variable, and generates a learned teacher model; and a learning data generating section that, by inputting a predetermined explanatory variable to the teacher model generated by the teacher learning section, acquires a predetermined objective variable with respect to the predetermined explanatory variable, and generates a combination of the predetermined explanatory variable and the predetermined objective variable as learning data for training a decision tree model. 
     Further, a learning program relating to the technique of the disclosure is a learning program for causing a computer to function as: a learning data acquiring section that acquires the learning data generated by the above-described learning data generating program; and a learning section that trains the decision tree model on the basis of the learning data acquired by the learning data acquiring section. 
     Further, a control program relating to the technique of the disclosure is a control program for causing a computer to function as: an information acquiring section that acquires the explanatory variable from the object of control; and a control section that, by inputting the explanatory variable acquired by the information acquiring section to the decision tree model learned by the above-described learning program, acquires an objective variable corresponding to the explanatory variable, and carries out control corresponding to the objective variable on the object of control. 
     Advantageous Effects of Invention 
     In accordance with the learning data generating device, the learning device, the control device, and the methods and programs relating to the technique of the disclosure, reducing the amount of learning data can be made easy. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block drawing illustrating functional structures of a control system relating to a present embodiment. 
         FIG.  2    is a block drawing illustrating hardware structures of a learning device relating to the present embodiment. 
         FIG.  3    is a block drawing illustrating hardware structures of a PLC relating to the present embodiment. 
         FIG.  4    is a drawing that schematically illustrates a decision tree model. 
         FIG.  5    is a drawing for explaining learning of the decision tree model. 
         FIG.  6    is a drawing for explaining a case of selecting several data from observed data. 
         FIG.  7    is a drawing for explaining generating of learning data. 
         FIG.  8    is a flowchart illustrating the flow of learning data generating processing in the present embodiment. 
         FIG.  9    is a flowchart illustrating the flow of learning processing in the present embodiment. 
         FIG.  10    is a flowchart illustrating the flow of control processing in the present embodiment. 
         FIG.  11    is a drawing for explaining a modified example of the present embodiment. 
         FIG.  12    is a drawing for explaining a modified example of the present embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     An example of an embodiment of the technique of the disclosure is described hereinafter with reference to the drawings. The present embodiment describes, as an example, a PLC (Programmable Logic Controller) in which is installed the control device relating to the technique of the disclosure. Note that, in the respective drawings, structural elements and portions are the same or equivalent are denoted by the same reference numerals. Further, the dimensions and ratios in the drawings are exaggerated for convenience of explanation, and there are cases in which they differ from the actual ratios. 
     As illustrated in  FIG.  1   , a control system  1  relating to the present embodiment has a production device  5 , a learning device  10  and a PLC  30 . The PLC  30  relating to the present embodiment controls the operations of the production device  5 , which is the object of control, by using a learned decision tree model that is generated by the learning device  10 . The production device  5  is, for example, a conveying device, a press machine, or the like. There may be one of or a plurality of the production device  5  that is the object of control. 
       FIG.  2    is a block drawing illustrating hardware structures of the learning device  10  relating to the present embodiment. As illustrated in  FIG.  2   , the learning device  10  has a CPU (Central Processing Unit)  42 , a memory  44 , a storage device  46 , an input/output I/F (Interface)  48 , a storage medium reading device  50 , and a communication I/F  52 . These structures are connected so as to be able to communicate with one another via a bus  54 . 
     A learning data generating program and a learning program, which are for executing respective processings that are described later, are stored in the storage device  46 . The CPU  42  is a central computing processing unit, and executes various programs and controls the respective structures. Namely, the CPU  42  reads-out programs from the storage device  46 , and executes the programs by using the memory  44  as a workspace. The CPU  42  carries out control of the above-described respective structures, and various computing processings, in accordance with the programs stored in the storage device  46 . 
     The memory  44  is structured from a RAM (Random Access Memory), and, as a workspace, temporarily stores programs and data. The storage device  46  is structured by a ROM (Read Only Memory) and an HDD (Hard Disk Drive), an SSD (Solid State Drive) or the like, and stores various programs, including the operating system, and various data. 
     The input/output I/F  48  is an interface that carries out input of data from the production device  5  and output of data to the production device  5 . Further, input devices for carrying out various types of input such as a keyboard, a mouse and the like for example, and an output devices for outputting various types of information, such as a display and a printer and the like for example, may be connected to the input/output I/F  48 . By employing a touch panel display as the output device, the output device may be made to function as the input device. 
     The storage medium reading device  50  carries out reading of data that are stored on various storage media such as a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disc)-ROM, a flexible disc, a USB (Universal Serial Bus) memory or the like, and writing of data to storage media, and the like. 
     The communication I/F  52  is an interface for communicating with other equipment, and standards such as, for example, Ethernet®, FDDI, Wi-Fi®, or the like are used thereat. 
       FIG.  3    is a block drawing illustrating hardware structures of the PLC  30  relating to the present embodiment. As illustrated in  FIG.  3   , the PLC  30  has a CPU  62 , a memory  64 , a storage device  66 , an input/output I/F  68 , a storage medium reading device  70 , and a communication I/F  72 . These structures are connected so as to be able to communicate with one another via a bus  74 . 
     A control program for executing the control processing that is described later is stored in the storage device  66 . The CPU  62  is a central computing processing unit, and executes various programs and controls the respective structures. Namely, the CPU  62  reads-out programs from the storage device  66 , and executes the programs by using the memory  64  as a workspace. The CPU  62  carries out control of the above-described respective structures, and various computing processings, in accordance with the programs stored in the storage device  66 . 
     The memory  64  is structured from a RAM, and, as a workspace, temporarily stores programs and data. The storage device  66  is structured by a ROM and an HDD, an SSD or the like, and stores various programs, including the operating system, and various data. 
     The input/output I/F  68  is an interface that carries out input of data from the production device  5  and output of data to the production device  5 . Further, input devices for carrying out various types of input such as a keyboard, a mouse and the like for example, and an output devices for outputting various types of information, such as a display and a printer and the like for example, may be connected to the input/output I/F  68 . By employing a touch panel display as the output device, the output device may be made to function as the input device. 
     The storage medium reading device  70  carries out reading of data that is stored on various storage media such as a CD-ROM, a DVD-ROM, a flexible disc, a USB memory or the like, and writing of data to storage media, and the like. 
     The communication I/F  72  is an interface for communicating with other equipment, and standards such as, for example, Ethernet®, FDDI, Wi-Fi®, or the like are used thereat. 
     The functional structures of the learning device  10  are described next. As illustrated in  FIG.  1   , the learning device  10  functionally includes an acquiring section  12 , a teacher learning section  16 , a learning data generating section  20 , a learning data acquiring section  24 , and a learning section  28 . Further, an observed data storing section  14 , a teacher model storing section  18 , a learning data storing section  22 , and a learning model storing section  26  are provided in a predetermined storage region of the learning device  10 . These respective functional structures are realized by the CPU  42  reading out respective programs that are stored in the storage device  46 , and expanding and executing the programs in the memory  44 . 
     The acquiring section  12  acquires plural observed data, which have been observed, from the production device  5  that is the object of the control. Then, the acquiring section  12  stores the plural observed data that are acquired in the observed data storing section  14 . The observed data of the present embodiment are data expressing combinations of explanatory variables and objective variables. 
     The explanatory variables of the observed data are information such as, for example, the number of revolutions of the motor within the production device  5 , sensor values detected by various sensors provided at the production device  5 , results of processing obtained by carrying out judging processing or the like on the basis of these values, and the like. The objective variables of the observed data are predicted values of states and the like of the production device  5  that are inferred with respect to the inputted explanatory variables. 
     On the basis of the explanatory variables among the observed data, the decision tree model of the present embodiment that will be described later outputs objective variables that are predicted values needed for control of the production device  5 . Namely, on the basis of the explanatory variables, the decision tree model of the present embodiment infers predicted values that are needed to control the production device  5 . Further, the PLC  30  that is described later generates control signals that correspond to the objective variables estimated from the decision tree model. The PLC  30  carries out control, which corresponds to the control signals generated from the objective variables, on the production device  5 . 
     The plural observed data that are acquired by the acquiring section  12  are stored in the observed data storing section  14 . 
     Here, a schematic drawing of the decision tree model of the present embodiment is illustrated in  FIG.  4   . As illustrated in  FIG.  4   , when learning is carried out in accordance with learning data LD, a learned decision tree model M is generated. When a given explanatory variable is inputted to this learned decision tree model M, an objective variable corresponding to the explanatory variable is obtained. In the learned decision tree model M illustrated in  FIG.  4   , the number of learning data LD is 10, and this is a model with a deep depth of layers. Because the learned decision tree model M illustrated in  FIG.  4    has a large number of learning data, the computation time is longer than, for example, a decision tree model in which the number of learning data is 3. 
     There are cases in which the computation time of the incorporated equipment, such as the PLC  30  of the present embodiment, is limited. Moreover, the incorporated equipment also have constraints relating to memory size. Therefore, the upper limit of the number of learning data that can be used in learning is determined in advance, and it is preferable to generate a decision tree model with as small a number of learning data as possible. Thus, in the present embodiment, the decision tree model is generated by using a small number of learning data. Specifics are described hereinafter. 
     A drawing for explaining learning of the decision tree model is illustrated in  FIG.  5   . The input on the horizontal axes of the graphs illustrated in  FIG.  5    shows the explanatory variables. Further, the output on the vertical axes of the graphs illustrated in  FIG.  5    shows the objective variables. As illustrated in  2 - 1  of  FIG.  5   , in a case in which observed data D that are pairs of an explanatory variable and an objective variable exists, the decision tree model M, which is a regression tree such as in  2 - 2 , is generated on the basis of these observed data D by an existing learning algorithm. 
     However, with the decision tree model M such as illustrated in  FIG.  5   , there is large number of observed data that are used at the time of generating the model, and therefore, the computation time is long. Thus, it has been thought to, for example, select several data from among the plural observed data, and train the decision tree model by using the selected observed data as the learning data. 
     A drawing for explaining a case of selecting several data from among observed data is illustrated in  FIG.  6   . As illustrated in  3 - 1  of  FIG.  6   , a case is considered in which plural observed data D are obtained. In this case, it is assumed that, as illustrated in  3 - 2  of  FIG.  6   , five data Ds for example are selected manually from the plural observed data D. However, as illustrated in  3 - 3  of  FIG.  6   , in a case in which noise is included in these data Ds, the accuracy of decision tree model M′ that is learned on the basis of the data Ds is low, and the model is not an appropriate model. 
     Thus, in the present embodiment, a teacher model that expresses a teacher decision tree model is generated once by using plural observed data, and learning data for training another decision tree model is generated by using this teacher model. 
     A drawing for explaining the generating of learning data in the present embodiment is illustrated in  FIG.  7   . As illustrated in  4 - 1  of  FIG.  7   , the teacher model M is generated on the basis of the plural observed data D by using an existing learning algorithm. Next, the number of learning data is decided upon. For example, in the example of  FIG.  7   , it is decided that three learning data are to be generated. 
     Then, in  4 - 2  of  FIG.  7   , range R of the explanatory variables at the time of generating the learning data is set. Specifically, as illustrated in  4 - 2  of  FIG.  7   , the upper limit of the explanatory variables and the lower limit of the explanatory variables among the plural observed data D are set, and this range is set as R. 
     Next, in  4 - 3  of  FIG.  7   , three explanatory variables X 1 , X 2 , X 3  that are to be inputted to the teacher model M are set. These three explanatory variables X 1 , X 2 , X 3  are explanatory variables that exist within the range R that has been set as the upper limit of the explanatory variable and the lower limit of the explanatory variables. Note that, in the example of  FIG.  7   , the three explanatory variables X 1 , X 2 , X 3  are set at a uniform interval. 
     Next, in  4 - 4  of  FIG.  7   , the three explanatory variables X 1 , X 2 , X 3  are inputted to the teacher model M, and objective variables Y 1 , Y 2 , Y 3  are outputted from the teacher model M. These three data P 1 , P 2 , P 3  are set as learning data. 
     Then, as illustrated in  4 - 5  of  FIG.  7   , another decision tree model M′ that is different than the teacher model M is generated on the basis of the learning data P 1 , P 2 , P 3 . Because this decision tree model M′ is a model that has been generated from the three learning data P 1 , P 2 , P 3 , the decision tree model M′ is a model of a short computation time. Moreover, at the decision tree model M′, because learning has been carried out on the basis of the learning data generated from the teacher model M, the decision tree model M′ is an accurate model. 
     Therefore, on the basis of the plural observed data that have been acquired by the acquiring section  12 , the teacher learning section  16  trains a model for outputting objective variables from explanatory variables, and generates a learned teacher model. Note that the present embodiment describes an example of a case in which a decision tree model is used as the teacher model. The decision tree model may be either of a classification tree or a regression tree. 
     The learned teacher model that has been generated by the teacher learning section  16  is stored in the teacher model storing section  18 . 
     Due to the learning data generating section  20  inputting a predetermined explanatory variable X to the learned teacher model that has been generated by the teacher learning section  16 , the learning data generating section  20  acquires a predetermined objective variable Y with respect to the predetermined explanatory variable X. Then, the learning data generating section  20  generates the combination of the predetermined explanatory variable X and the predetermined objective variable Y as learning data for training another decision tree model. 
     Specifically, first, as illustrated in above-described  FIG.  7   , the learning data generating section  20  sets the upper limit of the explanatory variables and the lower limit of the explanatory variables among the plural observed data. Next, as illustrated in above-described  FIG.  7   , the learning data generating section  20  sets, as predetermined explanatory variables, explanatory variables that exist within the range between the upper limit of the explanatory variables and the lower limit of the explanatory variables. 
     Then, as illustrated in above-described  FIG.  7   , the learning data generating section  20  inputs the predetermined explanatory variables that have been set to the teacher model that is stored in the teacher model storing section  18 . Due thereto, predetermined objective variables that correspond to the predetermined explanatory variables are outputted from the teacher model. The learning data generating section  20  stores the combinations of the predetermined explanatory variables X and predetermined objective variables Y in the learning data storing section  22  as learning data for training another decision tree model that is different than the teacher model. 
     Plural learning data that have been generated by the learning data generating section  20  are stored in the learning data storing section  22 . 
     At the time of training the another decision tree model that is different than the teacher model, the learning data acquiring section  24  acquires learning data that are stored in the learning data storing section  22 . 
     On the basis of the learning data acquired by the learning data acquiring section  24 , the learning section  28  trains the another decision tree model that is different than the teacher model. Note that, because the number of learning data is smaller than the number of observed data, the depth of the layers of the another decision tree model that is the object of learning is smaller than the depth of the layers of the teacher model. 
     The learned decision tree model that has been learned by the learning section  28  is stored in the learning model storing section  26 . 
     Functional structures of the PLC  10  relating to the present embodiment are described next. 
     As illustrated in above-described  FIG.  1   , the PLC  10  includes an information acquiring section  34  and a control section  36  as the functional structures thereof. Further, a control model storing section  32  is provided in a predetermined region of the PLC  10 . The respective functional structures are realized by the CPU  62  reading-out a control program stored in the storage device  66 , and expanding and executing the control program in the memory  64 . 
     The learned decision tree model that has been learned by the learning device  10  is stored in the control model storing section  32 . 
     The information acquiring section  34  acquires an explanatory variable outputted from the production device  5 . 
     By inputting the explanatory variable acquired by the information acquiring section  34  to the learned decision tree model stored in the control model storing section  32 , the control section  36  acquires the objective variable corresponding to that explanatory variable. Then, the control section  36  carries out control corresponding to the acquired objective variable on the production device  5 . 
     Specifically, on the basis of the objective variable outputted from the learned decision tree model, the control section  36  generates and outputs control signals for controlling the production device  5 . For example, in accordance with an objective variable that expresses a state of the production device  5  and that has been predicted by the decision tree model, the control section  36  generates control signals for adjusting the angles of rollers and changing the number of revolutions of the motor. Then, the control section  36  carries out control corresponding to the control signals on the production device  5 . 
     Operation of the control system  1  relating to the present embodiment is described next. 
     First, the processing of generating the learning data, which is executed at the learning device  10 , is described. At the time when the production device  5  is driven, the acquiring section  12  of the learning device  10  successively acquires observed data that is outputted from the production device  5 , and stores the observed data in the observed data storing section  14 . 
     Then, when the learning device  10  receives a signal instructing the generating of learning data, the CPU  42  of the learning device  10  reads-out the learning data generating program from the storage device  46 , and expands and executes the program in the memory  44 . Due thereto, the CPU  42  functions as the respective functional structures of the learning device  10 , and the learning data generating processing illustrated in  FIG.  8    is executed. 
     In step S 100 , the acquiring section  12  acquires the plural observed data that are stored in the observed data storing section  14 . 
     In step S 102 , on the basis of the plural observed data acquired in above-described step S 100 , the teacher learning section  16  trains a decision tree model for outputting an objective variable from an explanatory variable, and generates a learned teacher model. 
     In step S 104 , the teacher learning section  16  carries out evaluation of the accuracy of the learned teacher model generated in above-described step S 102 . For example, by using the plural observed data that were acquired in above-described step S 100 , the teacher learning section  16  computes the accuracy rate of the learned teacher model. 
     In step S 106 , the teacher learning section  16  judges whether or not the results of the evaluation of the accuracy that were obtained in above-described step S 104  satisfy a predetermined condition. For example, if the accuracy rate obtained in above-described step S 104  is greater than or equal to a predetermined threshold value, it is considered that the accuracy of the teacher model satisfies the predetermined condition, and the routine moves on to step S 108 . On the other hand, if the accuracy rate obtained in above-described step S 104  is less than the predetermined threshold value, it is considered that the accuracy of the teacher model does not satisfy the predetermined condition, and the routine moves on to step S 102  and repeats the learning. 
     In step S 108 , the teacher learning section  16  stores the learned teacher model, which was obtained in above-described step S 102 , in the teacher model storing section  18 . 
     In step S 110 , due to the learning data generating section  20  inputting a predetermined explanatory variable X to the learned teacher model stored in the teacher model storing section  18  in above-described step S 108 , the learning data generating section  20  acquires a predetermined objective variable Y with respect to the predetermined explanatory variable X. Then, the learning data generating section  20  generates the combination of the predetermined explanatory variable X and the predetermined objective variable Y as learning data for training another decision tree model. Note that the learning data generating section  20  generates plural learning data. 
     In step S 112 , the learning data generating section  20  stores the plural learning data that were generated in above-described step S 110  in the learning data storing section  22 , and ends the learning data generating processing. 
     The learning processing executed at the learning device  10  is described next. 
     After the plural learning data are stored in the learning data storing section  22 , when the learning device  10  receives a signal instructing the generating of a decision tree model, the CPU  42  of the learning device  10  reads-out the learning program from the storage device  46 , and expands and executes the program in the memory  44 . Due thereto, the CPU  42  functions as the respective functional structures of the learning device  10 , and the learning processing illustrated in  FIG.  9    is executed. 
     In step S 200 , the learning data acquiring section  24  acquires the plural learning data that are stored in the learning data storing section  22 . 
     In step S 202 , on the basis of the learning data acquired in above-described step S 200 , the learning section  28  trains another decision tree model that is different than the above-described teacher model. Note that the depth of the layers of the another decision tree model that is the object of learning is smaller than the depth of the layers of the above-described teacher model. 
     In step S 204 , the learning section  28  carries out evaluating of the accuracy of the learned decision tree model that was generated in above-described step S 202 . For example, by using plural observed data that are stored in the observed data storing section  14 , the teacher learning section  16  computes the accuracy rate of the learned decision tree model. 
     In step S 206 , the learning section  28  judges whether or not the results of the evaluation of the accuracy that were obtained in above-described step S 204  satisfy a predetermined condition. For example, if the accuracy rate obtained in above-described step S 204  is greater than or equal to a predetermined threshold value, it is considered that the accuracy of the decision tree model satisfies the predetermined condition, and the routine moves on to step S 208 . On the other hand, if the accuracy rate obtained in above-described step S 204  is less than the predetermined threshold value, it is considered that the accuracy of the decision tree model does not satisfy the predetermined condition, and the routine moves on to step S 202  and repeats the learning. 
     In step S 208 , the learning section  28  stores the learned decision tree model that was generated in above-described step S 202  in the learning model storing section  26 , and ends the learning processing. 
     The control processing executed at the PLC  30  is described next. 
     After the learned decision tree model is stored in the learning model storing section  26 , that learned decision tree model is inputted to the PLC  30 . The PLC  30  stores the learned decision tree model in the control model storing section  32 . 
     Then, when the PLC  30  receives a signal instructing the start of control of the production device  5 , the CPU  62  of the PLC  30  reads-out the control program from the storage device  66 , and expands and executes the program in the memory  64 . Due thereto, the CPU  62  functions as the respective functional structures of the PLC  30 , and the control processing illustrated in  FIG.  10    is executed. 
     In step S 300 , the information acquiring section  34  acquires an explanatory variable, such as the number of revolutions of the motor or the like, that is outputted from the production device  5 . 
     In step S 302 , due to the control section  36  inputting the explanatory variable acquired in above-described step S 300  to the learned decision tree model that is stored in the control model storing section  32 , the control section  36  acquires the objective variable corresponding to that explanatory variable. 
     In step S 304 , the control section  36  carries out control corresponding to the acquired objective variable on the production device  5 . Specifically, on the basis of the objective variable outputted from the learned decision tree model, the control section  36  generates and outputs control signals for controlling the production device  5 . Control processing using the decision tree model is thereby executed. 
     As described above, the learning device of the control system relating to the present embodiment acquires plural observed data that are observed data observed from an object of control and that express combinations of explanatory variables and objective variables. Then, on the basis of the plural observed data that have been acquired, the learning device trains a decision tree model for outputting objective variables from explanatory variables, and generates a learned teacher model. Then, by inputting predetermined explanatory variables to the teacher model, the learning device acquires predetermined objective variables with respect to the predetermined explanatory variables, and generates combinations of the predetermined explanatory variables and the predetermined objective variables as learning data for training a decision tree model. Due thereto, the amount of learning data can easily be reduced. Specifically, although there has conventionally been the need to select learning data from observed data by taking into consideration the quality and characteristics of the data used in learning, that labor is no longer necessary. 
     Further, learning data in which noise is reduced can be obtained. Specifically, learning data of good quality and on which the effects of noise are small are generated from the teacher model. 
     Further, the learning device of the present embodiment sets an upper limit of the explanatory variables and a lower limit of the explanatory variables, among the plural observed data. Then, the learning device sets explanatory variables, which exist within the range between the upper limit of the explanatory variables and the lower limit of the explanatory variables, as the predetermined explanatory variables, and inputs the predetermined explanatory variables that are set to the teacher model, and generates learning data. Due thereto, appropriate learning data can be generated within the range in which the observed data are obtained. With regard to this point, if the learning data are generated within a range in which the observed data is not obtained, and the decision tree model is trained on the basis of this learning data, this is not preferable from the standpoint of ensuring the operation of the PLC. Therefore, in accordance with the present embodiment, by generating learning data within the range in which the observed data are obtained, learning data that are appropriate also from the standpoint of ensuring operation of the PLC can be obtained. 
     Further, the learning device of the present embodiment trains another decision tree model on the basis of the learning data obtained from the teacher model. Due thereto, because the decision tree model is generated from learning data in which noise is reduced, a decision tree model of good accuracy can be obtained. Moreover, because the selecting of data that was carried out conventionally is unnecessary, the decision tree model can be generated easily. 
     Further, it is easy to apply the decision tree model at the time when the incorporated equipment are changed. Further, because learning data can be generated at any time from a teacher model that has been generated once, arbitrary changes to the size of the decision tree model are possible. 
     Further, the PLC of the present embodiment controls the object of control, which is a production device or the like, by using the decision tree model generated by the learning device. Therefore, accurate control is executed. Further, the object of control such as a production device or the like can be controlled by using the decision tree model that has a small amount of computation. 
     Note that, although the above-described embodiment describes an example in which the control device of the technique of the disclosure is installed in a PLC, the present disclosure is not limited to this. For example, the technique of the disclosure can also be applied to functions of autonomous driving or drive assist of a vehicle. In this case, data such as the amount of depression of the accelerator pedal or the brake pedal, the steering angle of the steering, the velocity, the acceleration or the like is acquired from the vehicle as an explanatory variable, and the explanatory variable is used as the input, and a predicted value that infers the state of the vehicle is outputted as the objective variable from the decision tree model. Then, it suffices to output control signals that are based on the objective variable to the vehicle. 
     Further, the above embodiment describes, as an example, a case in which the learning data is generated within the range R between the upper limit of the explanatory variables and the lower limit of the explanatory variables, as illustrated in  4 - 3  of  FIG.  7   . However, the present disclosure is not limited to this. For example, as illustrated in  FIG.  11   , an explanatory variable, which is explanatory variable that exists within range R 1  between the upper limit of the explanatory variables and the lower limit of the explanatory variables and that exists within range R 2  in which the concentration of the observed data is greater than or equal to a threshold value, may be set as the predetermined explanatory variable X. In this case, because the number of observed data that exist within the range R 2  is a predetermined density or more, learning data that approximate the observed data are obtained when the learning data are generated within this range, and this is more preferable. In this case, as illustrated in  FIG.  11   , the predetermined explanatory variable X that is set is inputted to the teacher model M, and the learning data P, which is the combination of the explanatory variable X and the objective variable Y, is generated. 
     Further, although the above embodiment describes an example in which the learning processing is ended in a case in which the accuracy of the teacher model and the accuracy of the decision tree model are predetermined levels or higher, the present disclosure is not limited to this. For example, as illustrated in  FIG.  12   , the control system  1  may further have a display device  29 , and a user may confirm the results of learning that are displayed on the display device  29 . In this case, the user may confirm the accuracy of the teacher model and the accuracy of the decision tree model that are displayed on the display device  29 , and may decide whether or not the learning may be ended. 
     Although the above embodiment describes, as an example, a case in which a decision tree model is used as the teacher model, the present disclosure is not limited to this. For example, a model of a type that is different than a decision tree model may be used as the teacher model. 
     Further, any of various types of processors other than a CPU may execute the respective processings that are executed due to the CPU reading software (programs) in the above-described embodiment. Examples of processors in this case include PLDs (Programmable Logic Devices) whose circuit structure can be changed after production such as FPGAs (Field-Programmable Gate Arrays) and the like, and dedicated electrical circuits that are processors having circuit structures that are designed for the sole purpose of executing specific processings such as ASICs (Application Specific Integrated Circuits) and the like, and the like. Further, the respective processings may be executed by one of these various types of processors, or may be executed by a combination of two or more of the same type or different types of processors (e.g., plural FPGAs, or a combination of a CPU and an FPGA, or the like). Further, the hardware structures of these various types of processors are, more specifically, electrical circuits that combine circuit elements such as semiconductor elements and the like. 
     Further, although the above embodiment describes, as an example, a case of being structured by the learning device  10  and the PLC  30 , the present disclosure is not limited to this. For example, the learning device  10  and the PLC  30  may be structured as a single device. Further, the learning data generating processing of the learning device  10  may be structured as a learning data generating device, and the learning processing of the learning device  10  may be structured as a learning device. 
     Although the above embodiment describes an aspect in which the respective programs are stored in advance (e.g., are installed) in a storage device, the present disclosure is not limited to this. The programs may be provided in a form of being stored in a storage medium such as a CD-ROM, a DVD-ROM, a flexible disc, a USB memory, or the like. Further, the programs may be in a form that is downloaded from an external device over a network. 
     The disclosure of Japanese Patent Application No. 2020-044804 filed on Mar. 13, 2020 is, in its entirety, incorporated by reference into the present specification. All publications, patent applications, and technical standards mentioned in the present specification are incorporated by reference into the present specification to the same extent as if such individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.