CA2066458A1 - Computer neural network process measurement and control system and method - Google Patents

Computer neural network process measurement and control system and method

Info

Publication number
CA2066458A1
CA2066458A1 CA002066458A CA2066458A CA2066458A1 CA 2066458 A1 CA2066458 A1 CA 2066458A1 CA 002066458 A CA002066458 A CA 002066458A CA 2066458 A CA2066458 A CA 2066458A CA 2066458 A1 CA2066458 A1 CA 2066458A1
Authority
CA
Canada
Prior art keywords
neural network
training data
control system
produced
predicted value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CA002066458A
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French (fr)
Other versions
CA2066458C (en
Inventor
Richard D. Skeirik
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Rockwell Automation Pavilion Inc
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Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Family has litigation
First worldwide family litigation filed litigation Critical https://patents.darts-ip.com/?family=24249095&utm_source=google_patent&utm_medium=platform_link&utm_campaign=public_patent_search&patent=CA2066458(A1) "Global patent litigation dataset” by Darts-ip is licensed under a Creative Commons Attribution 4.0 International License.
Application filed by Individual filed Critical Individual
Publication of CA2066458A1 publication Critical patent/CA2066458A1/en
Application granted granted Critical
Publication of CA2066458C publication Critical patent/CA2066458C/en
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

Links

Classifications

    • 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
    • G05B13/027—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 using neural networks only
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00—Data processing: artificial intelligence
    • Y10S706/902—Application using ai with detail of the ai system
    • Y10S706/903—Control
    • Y10S706/906—Process plant

Abstract

A computer neural network process control system and method provides predicted values to a controller used to control a process for producing a product. The predicted value is produced by a neural network provided with stored input data from a historical database. The neural network is configured by a developer using a neural network configura-tion stage and step. The neural network is trained using training data stored in the historical database, which is com-pared with the predicted value produced by the neural ne-twork using input data obtained at a time associated with the time of the training data. An error signal is produced by the comparison of the predicted value with the training data, the error signal is used to train the neural network, and is al-so used to determine (1) whether the predicted values of the neural network should continue to be used by the controller, and (2) to retrain the network. Predicted values are thus pro-duced by the neural network which have a close correlation to the training values training data.
CA002066458A 1990-08-03 1991-07-25 Computer neural network process measurement and control system and method Expired - Lifetime CA2066458C (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US07/563,095 1990-08-03
US07/563,095 US5282261A (en) 1990-08-03 1990-08-03 Neural network process measurement and control
PCT/US1991/005259 WO1992002866A1 (en) 1990-08-03 1991-07-25 Computer neural network process measurement and control system and method

Publications (2)

Publication Number Publication Date
CA2066458A1 true CA2066458A1 (en) 1992-02-04
CA2066458C CA2066458C (en) 2003-04-22

Family

ID=24249095

Family Applications (1)

Application Number Title Priority Date Filing Date
CA002066458A Expired - Lifetime CA2066458C (en) 1990-08-03 1991-07-25 Computer neural network process measurement and control system and method

Country Status (5)

Country Link
US (1) US5282261A (en)
EP (1) EP0495044B1 (en)
CA (1) CA2066458C (en)
DE (1) DE69130253T2 (en)
WO (1) WO1992002866A1 (en)

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