WO2013171862A1 - Dispositif d'apprentissage de système de calcul de réglage et procédé d'apprentissage associé - Google Patents

Dispositif d'apprentissage de système de calcul de réglage et procédé d'apprentissage associé Download PDF

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WO2013171862A1
WO2013171862A1 PCT/JP2012/062535 JP2012062535W WO2013171862A1 WO 2013171862 A1 WO2013171862 A1 WO 2013171862A1 JP 2012062535 W JP2012062535 W JP 2012062535W WO 2013171862 A1 WO2013171862 A1 WO 2013171862A1
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learning
function
value
calculation
downstream
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PCT/JP2012/062535
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English (en)
Japanese (ja)
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治樹 井波
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東芝三菱電機産業システム株式会社
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Priority to PCT/JP2012/062535 priority Critical patent/WO2013171862A1/fr
Priority to KR1020147031254A priority patent/KR101622068B1/ko
Priority to JP2014515412A priority patent/JP5846303B2/ja
Priority to CN201280073247.5A priority patent/CN104303114B/zh
Publication of WO2013171862A1 publication Critical patent/WO2013171862A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B21/00—Systems involving sampling of the variable controlled
    • G05B21/02—Systems involving sampling of the variable controlled electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present invention relates to a learning apparatus and learning method for a setting calculation system.
  • a mathematical model that represents and reproduces a physical phenomenon that occurs in an environment including the controlled object by a mathematical formula is constructed.
  • a method for determining a setting value by obtaining a setting value that can achieve the target result is known.
  • the actual value calculated by the mathematical model is compared with the actual value obtained from the actual value actually measured with a measuring instrument, etc.
  • a method of updating the learning term of the mathematical model is often adopted so that the difference between the two becomes small.
  • the factor that causes the difference between the actual calculation value by the mathematical model and the actual actual value is processed by the process line and the time-series fluctuation of the process line due to the change of equipment and environment. It is known that the calculation value of the mathematical formula model is corrected based on the learning coefficient calculated for each of these two types of variations after roughly dividing into two types of variations due to the types of materials and processing patterns. (For example, refer to Patent Document 1).
  • the problem that the mathematical model learning cannot be completed due to a deficiency in a part of the actual values necessary for learning, and the prediction accuracy of the mathematical model temporarily decreases is the conventional setting. It exists in a learning apparatus and a learning method of a calculation system. In addition, in the situation immediately after starting learning, if the learning of the mathematical model cannot be completed due to a deficiency in a part of the actual values necessary for learning, the mathematical model is expected to have the expected accuracy. There is also a problem that the time required until the prediction can be performed becomes long.
  • the present invention has been made to solve such a problem, and even when a part of the actual value necessary for learning the mathematical model is not obtained, the actual value for the obtained amount is used.
  • a learning apparatus and learning method for a setting calculation system capable of appropriately updating learning terms of each function constituting a mathematical model by performing learning and suppressing a decrease in accuracy of the mathematical model Is.
  • the learning device updates the learning term of the mathematical model using the actual measurement value.
  • the learning term calculation unit that calculates the learning term of each of the upstream function and the downstream function constituting the mathematical model using the actual measurement value
  • the actual measurement value determination unit that determines whether the first actual measurement value is abnormal, or the first actual measurement value is abnormal
  • An actual calculation value calculation unit that inputs an output from the upstream function to the downstream function and calculates an actual calculation value output from the downstream function, and the actual measurement corresponding to the output from the downstream function Value
  • the learning method of the setting calculation system in the setting calculation system for calculating the setting value of the mechanical equipment using the mathematical model, the learning method of updating the learning term of the mathematical model using the actual measurement value.
  • Fruit Comprising an error of the actual calculated value for the second measured value is a value
  • the learning device and the learning method of the setting calculation system according to the present invention even when a part of the actual value necessary for learning the mathematical model is not obtained, the learning using the actual value for the obtained amount is performed. It is possible to appropriately update the learning term of each function constituting the mathematical model, and it is possible to suppress a decrease in accuracy of the mathematical model.
  • FIG. FIGS. 1 to 3 relate to Embodiment 1 of the present invention
  • FIG. 1 is a diagram for explaining the overall configuration of the setting calculation system
  • FIG. 2 is a flowchart for explaining the operation of the learning device of the setting calculation system
  • FIG. 3 is a diagram illustrating a detailed configuration centering on a learning device provided in the setting calculation system.
  • FIG. 1 shows an overall configuration of a setting calculation system 2 that calculates set values of mechanical equipment 1 constituting a process line of a rolling plant, for example.
  • the setting calculation system 2 includes a setting calculation device 3, an actual measurement value collection device 4, and a learning device 5.
  • the setting calculation device 3 a mathematical model in which a physical phenomenon generated by the operation of the machine facility 1 is modeled by a mathematical formula is registered in advance.
  • the setting calculation device 3 uses the mathematical model to simulate the result of the operation of the machine facility 1, thereby calculating the set value of the machine facility 1 so that the operation result of the machine facility 1 is closer to the target value.
  • the set value calculated by the setting calculation device 3 is output to the machine facility 1.
  • the mechanical equipment 1 operates according to the set value calculated by the setting calculation system 2.
  • the measured value collection device 4 collects measured values of predetermined types of physical quantities necessary for learning the mathematical model in the learning device 5 among the physical quantities in the machine equipment 1 and the environment in which the machine equipment 1 operates. It is. In the environment in which the mechanical equipment 1 or the mechanical equipment 1 is installed, a measuring instrument or the like for measuring the physical quantity is installed in advance. The actual measurement value collection device 4 collects the actual measurement values of the physical quantities measured by a measuring instrument or the like.
  • the learning device 5 performs learning of the mathematical model used by the setting calculation device 3 based on the actual measurement values collected by the actual measurement value collection device 4.
  • the learning device 5 includes a mathematical model learning calculation unit 6, an actual value determination unit 7, and a complementary learning calculation unit 8.
  • the formula model learning calculation unit 6 calculates the actual calculation value using the same formula model as that used in the setting calculation device 3.
  • the actual calculation value is an output from the mathematical model when the actual measurement value collected by the actual measurement value collection device 4 is input.
  • the mathematical model learning calculation unit 6 compares the actual calculation value calculated from the mathematical model with the actual value obtained directly from the actual measurement value collected by the actual measurement value collection device 4, and the difference between these values is reduced.
  • the learning term of the mathematical model is calculated as follows.
  • the actual measurement value determination unit 7 determines whether the actual measurement value collected by the actual measurement value collection device 4 is normal or abnormal.
  • the state where the actual measurement value is abnormal is a state where the actual measurement value deviates from the normal range (this range is determined in advance for each measurement target), and the actual measurement value itself does not exist. State is also included.
  • the learning device 5 replaces the actual measurement value when the actual calculation value corresponding to the actual measurement value determined to be abnormal exists.
  • the learning term is calculated using the actual calculation value.
  • a mathematical model that reproduces the physical phenomenon may be configured by combining a plurality of simpler functions.
  • upstream function an output from a certain function
  • downstream function another function
  • the update of the learning term in the downstream function is normally performed by inputting the actual value collected by the actual value collection device 4 to the downstream function. This is done using the actual calculation value output from the downstream function.
  • the actual measurement value to be input to the downstream function is abnormal, the actual measurement value cannot be input to the downstream function, so that the downstream function cannot be learned.
  • the downstream function learns in the downstream function by inputting the actual calculation value output from the upstream function to the downstream function instead of the actual measurement value. That is, the above-mentioned “actual calculation value corresponding to the actual measurement value” is the actual calculation value of the upstream function used as the input of the downstream function in the mathematical model.
  • the complementary learning calculation unit 8 When the actual value to be input to the downstream function is determined by the actual value determination unit 7 to be abnormal, the complementary learning calculation unit 8 outputs the downstream function and the actual value corresponding to this output. First, the error is calculated. Then, this error is distributed to the learning terms of both the upstream function and the downstream function with the distribution ratio obtained by a predetermined procedure.
  • the procedure for obtaining the error distribution ratio will be explained.
  • the error between the output of the mathematical model and the actual value becomes smaller as the learning of the mathematical model progresses. Therefore, in a state where learning of the mathematical model is sufficiently advanced, a change when the learning term is updated becomes small.
  • the relative size of the learning term of each function can be evaluated using the size of each function as a guide.
  • the size of a function is the size of an output from the function based on the input to the function.
  • the complementary learning calculation unit 8 obtains the magnitude of the output from the function based on the input to the function for each of the upstream function and the downstream function. Then, the ratio between the magnitude of the upstream function and the magnitude of the downstream function thus obtained is set as the distribution ratio.
  • the measured value collected by the measured value collecting device 4 is used in principle. However, when the actual measurement value to be input is abnormal and cannot be used, another value such as a result calculation value may be substituted.
  • the complementary learning calculation unit 8 distributes the error to the learning terms of both the upstream function and the downstream function with the distribution ratio thus obtained. Then, the mathematical model learning calculation unit 6 updates each learning term of the upstream function and the downstream function based on the error distributed by the complementary learning calculation unit 8.
  • the setting calculation device 3 and the learning device 5 will be described in more detail with reference to FIG.
  • the mathematical model is composed of two functions, that is, an upstream function and a downstream function, and as shown in FIG.
  • the downstream function is further composed of a model expression 2a and a model expression 2b.
  • the setting calculation device 3 calculates the set value of the machine facility 1 so that the output of the mathematical model representing the result of the operation of the machine facility 1 is closer to the target value.
  • the calculation of the output of the mathematical model will be described step by step.
  • the function of the model formula 1a is f
  • the input physical quantity is V 0
  • the intermediate result output Y 1 from the model equation 1a is expressed by the following equation (1).
  • Equation (2) H is an error correction function
  • Z 1 is a coefficient of a learning term
  • Equation (3) g is a function of model equation 1b
  • W 1 l (l 1, 2). ,... Are other variable inputs
  • V 1 output from the upstream function is input to the downstream function, and the calculation of the mathematical model proceeds.
  • the function of the model formula 2a is f
  • the intermediate result output Y 2 from the model equation 2a is expressed by the following equation (4).
  • Equation (6) is input with Y 2 Z obtained as a result of correcting the intermediate result output Y 2 by the learning term expressed by the following equation (5) as the input of the model equation 2b.
  • V 2 that is an output from the downstream function is obtained.
  • the V 2 is the final result output from the mathematical model.
  • H is an error correction function
  • Z 2 is a coefficient of a learning term
  • g is a function of model equation 2b
  • W 2 l (l 1, 2). ,... Are other variable inputs
  • the setting calculation device 3 determines the set value of the mechanical equipment 1 by obtaining X 1 i and X 2 i such that the final result output V 2 obtained in this way is equal to the target value V AIM . That is, X 1 i and X 2 i satisfying the expressions (Expression 1) to (Expression 7) are obtained after placing them as the following Expression (7).
  • the mathematical model learning calculation unit 6 included in the learning device 5 includes an actual calculation value calculation unit 6a and a learning term calculation unit 6b.
  • the actual calculation value calculation unit 6 a calculates the actual calculation value using the same mathematical model as that used in the setting calculation device 3.
  • the learning term calculation unit 6b compares the actual calculation value calculated by the actual calculation value calculation unit 6a with the actual value obtained by the actual measurement value collected by the actual measurement value collection device 4 so that the difference between these values is reduced. Calculate the learning term of the mathematical model.
  • the calculation of the actual calculation value in the actual calculation value calculation unit 6a will be described first. Since the calculation of the actual calculation value is basically the same for the upstream function and the downstream function, the subscripts “1” and “2” representing the upstream function and the downstream function are omitted.
  • the function of the model formula a is f
  • the actual value of the input physical quantity is V ACT
  • the intermediate result output (actual calculation value) Y ACAL from the model equation a is expressed by the following equation (8).
  • Equation 10 Y ACAL Z , which is the result of correcting the halfway result output Y ACAL by the learning term expressed by the following equation (9), is obtained. Then, using the obtained Y ACAL Z as an input of the model formula b, the following formula (Equation 10) is calculated to obtain the actual calculated value V ACAL .
  • H is an error correction function
  • Z is a coefficient of a learning term
  • Equation (10) g is a function of model equation b
  • the learning term calculation is the same for the upstream function and the downstream function in principle, as in the case of the actual calculation value.
  • the learning term is applied to the intermediate result output from the model equation a. Therefore, the comparison between the actual calculation value and the actual value for learning is performed in the intermediate result output from the model formula a.
  • the actual value Y ACT corresponding to intermediate results output from the model formula a is calculated by the following equation (11).
  • the error Z CUR is calculated by the following equation (12) from the actual value Y ACT thus obtained and Y ACAL obtained by the equation (8).
  • h is an error calculation function.
  • the specific form of the error correction function H in the expressions (Equation 2), (Equation 5), and (Equation 9) changes. That is, when the error calculation function h takes the difference of the input variables, the error correction function H takes the sum of the input variables, and when the error calculation function h takes the ratio of the input variables, The error correction function H is a product of input variables.
  • this error is smoothed by the following equation (13) and reflected in the learning term to calculate a new learning term Z NEW .
  • Z NEW is a learning term used in the next setting calculation in the setting calculation device 3
  • Z OLD is a learning term used in the previous setting calculation in the setting calculation device 3
  • ⁇ is a smoothing. It is a coefficient.
  • the above is the calculation method of the learning term when the actual measurement value necessary for the calculation of the learning term can be normally obtained.
  • the actual measurement value input to the downstream function is abnormal in the calculation of the learning term of the downstream function
  • the actual calculation value output from the upstream function is converted to the downstream function.
  • the actual calculation value necessary for learning in the downstream function is obtained.
  • calculation of the actual calculation value necessary for learning in the downstream function when the actual measurement value input to the downstream function is abnormal in the calculation of the learning term of the downstream function will be described.
  • the complementary learning calculation unit 8 converts the error (Y ACT ⁇ Y 2 ACAL ) into the upstream function. And to the learning terms of both the downstream function.
  • the calculation method of the error distribution ratio in the complementary learning calculation unit 8 varies depending on the form of the error calculation function h in the equation (12). Specifically, when the error calculation function h takes the difference of the input variables, the error distribution in the complementary learning calculation unit 8 is proportional distribution based on the following equations (15) and (16). Is done.
  • the error distribution in the complementary learning calculation unit 8 is performed by proportional distribution based on the following equations (17) and (18). .
  • Z 1CUR is an error distributed to the learning term of the upstream function
  • Z 2CUR is an error distributed to the learning term of the upstream function
  • f is It is a function of the model formula a.
  • the error is proportionally distributed to the learning terms of the upstream function and the downstream function in this way, the error distributed to each learning term of the upstream function and the downstream function is smoothed by the equation (13). And then reflected in the learning terms.
  • the updated new learning term is used for setting value calculation in the setting calculation device 3 at the next setting timing.
  • step S1 the actual measurement value collection device 4 collects actual measurement values.
  • step S2 the measured value determination part 7 confirms whether V2ACT is abnormal among the measured values which the measured value collection device 4 collected. If V2ACT is not abnormal, the process proceeds to step S3.
  • step S ⁇ b> 3 the actual measurement value determination unit 7 confirms whether or not V 1ACT is abnormal among the actual measurement values collected by the actual measurement value collection device 4. If V 1ACT is not abnormal, the process proceeds to step S4.
  • step S4 the result calculation value calculation unit 6a calculates the result calculation value Y1ACAL of the upstream function using the equation (8).
  • step S5 the learning term calculation unit 6b calculates the actual value Y1ACT of the upstream function using the equation (11).
  • step S6 the learning term calculation unit 6b calculates the error Z1CUR of the upstream function using the equation (12).
  • step S6 the process proceeds to step S7.
  • step S7 the actual calculated value calculating section 6a calculates the actual calculated value Y 2ACAL downstream function by substituting the measured values V 1ACT the V ACT of (number 8).
  • step S8 the learning term calculation unit 6b calculates the actual value Y2ACT of the downstream function using the equation (11).
  • step S9 the learning term calculation unit 6b calculates the error Z 2CUR of the downstream function using the equation (12).
  • step S10 the learning term calculation unit 6b (number 13) of the by substituting Z 1CUR and Z 2CUR respectively Z CUR, the learning term of learning term Z 1 NEW and downstream function of the upstream function Z 2NEW Calculate Then, the process proceeds to step S11, and updates the learning term of the calculated learning term Z 1 NEW and mathematical models of setting calculation device 3 in Z 2NEW, a series of learning term updating process ends.
  • step S20 the actual calculation value calculation unit 6a calculates the actual calculation value Y1ACAL of the upstream function using the equation (8).
  • step S21 the actual calculation value calculation unit 6a calculates the actual function calculation value V1ACAL of the upstream function using Equation (9) and Equation (10).
  • step S21 the process proceeds to step S22.
  • step S22 actual calculated value calculating section 6a calculates the actual calculated value Y 2ACAL downstream functions using V 1ACAL by equation (14) below.
  • step S23 the learning term calculation unit 6b calculates the actual value Y2ACT of the downstream function using the equation (11).
  • step S24 the complementary learning calculation unit 8 (number 15) and (Expression 16), or by using the equation (17) and (Equation 18), calculates the Z 1CUR and Z 2CUR To do.
  • step S24 the process proceeds to step S10 described above.
  • step S2 if V2ACT is abnormal, the process proceeds to step S30.
  • the actual measurement value determination unit 7 confirms whether or not V1ACT is abnormal among the actual measurement values collected by the actual measurement value collection device 4. If V 1ACT is also abnormal, the learning term is not updated in both the upstream function and the downstream function.
  • step S31 the actual calculation value calculation unit 6a calculates the actual function calculation value Y1ACAL of the upstream function using the equation (8).
  • step S32 the learning term calculation unit 6b calculates the actual value Y1ACT of the upstream function using the equation (11).
  • step S33 the learning term calculation unit 6b calculates the error Z 1CUR of the upstream function using the equation (12).
  • step S34 the learning term calculation unit 6b calculates the learning term Z1NEW of the upstream function using the equation (13). And it progresses to step S35, the learning term of the numerical formula model of the setting calculation apparatus 3 is updated by the calculated learning term Z1NEW , and a series of learning term update processes are complete
  • the learning device of the setting calculation system configured as described above includes a learning term calculation unit that calculates the learning terms of the upstream function and the downstream function that constitute the mathematical model using actual measurement values, and a learning term calculation unit.
  • a learning term calculation unit that calculates the learning terms of the upstream function and the downstream function that constitute the mathematical model using actual measurement values
  • a learning term calculation unit In the calculation of the learning term of the downstream function, an actual value determination unit that determines whether or not the first actual value input to the downstream function is abnormal is determined, and the first actual value is determined to be abnormal
  • the actual calculation value calculation unit for calculating the actual calculation value output from the downstream function, and the second corresponding to the output from the downstream function
  • a complementary learning calculation unit that distributes an error of the actual calculation value with respect to the actual measurement value to the learning term of the upstream function and the learning term of the downstream function.
  • the actual measurement value to be input to the downstream function for learning is not obtained, the actual calculation value output from the upstream function is input to the downstream function instead of the actual measurement value.
  • the accumulated error is distributed to the learning term of the upstream function, thereby realizing appropriate learning in both the upstream function and the downstream function.
  • the learning device of the setting calculation system configured as described above includes, for example, functions performed by each unit of the mathematical model learning calculation unit, the actual calculation value calculation unit, the learning term calculation unit, the actual measurement value determination unit, and the complementary learning calculation unit. It is also possible to construct information processing for realizing the above by causing a hardware resource having a central processing unit, a storage device, and the like to execute.
  • the present invention can be used in a learning apparatus and a learning method for updating a learning term of a mathematical model using an actual measurement value in a setting calculation system that calculates a setting value of a mechanical facility using a mathematical model.

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Abstract

La présente invention porte sur un dispositif d'apprentissage de système de calcul de réglage avec lequel, même si une partie de valeurs mesurées réelles qui sont nécessaires pour l'apprentissage de modèle mathématique ne peut pas être obtenue, il est possible d'actualiser de manière appropriée des termes d'apprentissage de chaque fonction qui configure le modèle mathématique. A cette fin, un dispositif d'apprentissage, qui, à l'aide d'une valeur mesurée réelle, actualise un terme d'apprentissage d'un modèle mathématique avec un système de calcul de réglage qui, à l'aide du modèle mathématique, calcule une valeur de réglage d'équipement mécanique, comprend : une unité de calcul de terme d'apprentissage qui calcule respectivement, à l'aide de valeurs mesurées réelles, des termes d'apprentissage de fonctions côté amont et côté aval qui configurent un modèle mathématique ; une unité de détermination de valeur mesurée réelle qui, dans le calcul du terme d'apprentissage de la fonction côté aval, détermine si une première valeur mesurée réelle qui est mise en entrée dans la fonction côté aval est anormale ; une unité de calcul de valeur de calcul de performance qui, lorsque l'on détermine que la première valeur mesurée réelle est anormale, met en entrée une sortie provenant de la fonction côté amont dans la fonction côté aval et calcule une valeur de calcul de performance qui est délivrée en sortie par la fonction côté aval ; et une unité de calcul d'apprentissage complémentaire qui affecte à chaque terme d'apprentissage de la fonction côté amont et côté aval une erreur de la valeur de calcul de performance par rapport à la seconde valeur mesurée réelle qui correspond à la sortie provenant de la fonction côté aval.
PCT/JP2012/062535 2012-05-16 2012-05-16 Dispositif d'apprentissage de système de calcul de réglage et procédé d'apprentissage associé WO2013171862A1 (fr)

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KR1020147031254A KR101622068B1 (ko) 2012-05-16 2012-05-16 설정 계산 시스템의 학습 장치 및 학습 방법
JP2014515412A JP5846303B2 (ja) 2012-05-16 2012-05-16 設定計算システムの学習装置及び学習方法
CN201280073247.5A CN104303114B (zh) 2012-05-16 2012-05-16 设定计算系统的学习装置以及学习方法

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2015122010A1 (ja) * 2014-02-17 2017-03-30 東芝三菱電機産業システム株式会社 圧延プロセスの学習制御装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH02214970A (ja) * 1989-02-15 1990-08-27 Toshiba Corp 対象システムモデリング方法及び対象システムモデリング装置
JPH07200005A (ja) * 1993-12-28 1995-08-04 Mitsubishi Electric Corp 学習制御方法
JP2002091505A (ja) * 2000-09-14 2002-03-29 Toshiba Corp モデル同定装置
JP2003340508A (ja) * 2002-05-27 2003-12-02 Toshiba Ge Automation Systems Corp 圧延機設定計算装置用学習制御装置
JP2004050211A (ja) * 2002-07-18 2004-02-19 Toshiba Mitsubishi-Electric Industrial System Corp 圧延プロセスのモデル学習装置
JP2012043235A (ja) * 2010-08-20 2012-03-01 Hitachi Ltd 工場のモデル構築支援システム

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2839746B2 (ja) * 1991-06-17 1998-12-16 株式会社神戸製鋼所 プロセスラインにおける学習制御方法
US5566275A (en) * 1991-08-14 1996-10-15 Kabushiki Kaisha Toshiba Control method and apparatus using two neural networks
JPH06342422A (ja) * 1993-05-31 1994-12-13 Kawasaki Steel Corp 信号処理方法
JPH09265302A (ja) * 1996-03-28 1997-10-07 Kawasaki Steel Corp プロセスモデル同定方法
US5764509A (en) * 1996-06-19 1998-06-09 The University Of Chicago Industrial process surveillance system
JP2000242323A (ja) * 1999-02-24 2000-09-08 Hitachi Ltd プラント運転ガイダンスシステム
US7720641B2 (en) * 2006-04-21 2010-05-18 Exxonmobil Research And Engineering Company Application of abnormal event detection technology to delayed coking unit
JP5069608B2 (ja) * 2008-05-26 2012-11-07 株式会社日立製作所 熱間圧延機の板幅制御装置および制御方法
CN101966535B (zh) * 2009-07-28 2012-11-14 宝山钢铁股份有限公司 一种基于来料板廓的冷轧板形前馈控制设定方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH02214970A (ja) * 1989-02-15 1990-08-27 Toshiba Corp 対象システムモデリング方法及び対象システムモデリング装置
JPH07200005A (ja) * 1993-12-28 1995-08-04 Mitsubishi Electric Corp 学習制御方法
JP2002091505A (ja) * 2000-09-14 2002-03-29 Toshiba Corp モデル同定装置
JP2003340508A (ja) * 2002-05-27 2003-12-02 Toshiba Ge Automation Systems Corp 圧延機設定計算装置用学習制御装置
JP2004050211A (ja) * 2002-07-18 2004-02-19 Toshiba Mitsubishi-Electric Industrial System Corp 圧延プロセスのモデル学習装置
JP2012043235A (ja) * 2010-08-20 2012-03-01 Hitachi Ltd 工場のモデル構築支援システム

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2015122010A1 (ja) * 2014-02-17 2017-03-30 東芝三菱電機産業システム株式会社 圧延プロセスの学習制御装置

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