CA2066278A1 - Computer neural network supervisory process control system and method - Google Patents

Computer neural network supervisory process control system and method

Info

Publication number
CA2066278A1
CA2066278A1 CA002066278A CA2066278A CA2066278A1 CA 2066278 A1 CA2066278 A1 CA 2066278A1 CA 002066278 A CA002066278 A CA 002066278A CA 2066278 A CA2066278 A CA 2066278A CA 2066278 A1 CA2066278 A1 CA 2066278A1
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CA
Canada
Prior art keywords
neural network
input data
training
data
setpoint
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
CA002066278A
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French (fr)
Other versions
CA2066278C (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
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First worldwide family litigation filed litigation Critical https://patents.darts-ip.com/?family=24245536&utm_source=google_patent&utm_medium=platform_link&utm_campaign=public_patent_search&patent=CA2066278(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 CA2066278A1 publication Critical patent/CA2066278A1/en
Application granted granted Critical
Publication of CA2066278C publication Critical patent/CA2066278C/en
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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 neural network for adjusting a setpoint in process control replaces a human operator. The neural network operates in three modes: training, operation, and retraining. In operation, the neu-ral network is trained using training input data along with input data. The input data is from the sensor(s) monitoring the process.
The input data is used by the neural network to develop output data. The training input data are the setpoint adjustments made by a human operator. The output data is compared with the train-ing input data to produce error data, which is used to adjust the.
weights of the neural network so as to train it. After training has been completed, the neural network enters the operation mode. In this mode, the present invention uses the input data to predict out-put data used to adjust the setpoint supplied to the regulatory con-troller. Thus, the operator is effectively replaced. The present in-vention in the retraining mode utilizes new training input data to retrain the neural network by adjusting the weight(s).
CA002066278A 1990-08-03 1991-07-25 Computer neural network supervisory process control system and method Expired - Lifetime CA2066278C (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US07/562,268 US5142612A (en) 1990-08-03 1990-08-03 Computer neural network supervisory process control system and method
US07/562,268 1990-08-03
PCT/US1991/005254 WO1992002865A1 (en) 1990-08-03 1991-07-25 Computer neural network supervisory process control system and method

Publications (2)

Publication Number Publication Date
CA2066278A1 true CA2066278A1 (en) 1992-02-04
CA2066278C CA2066278C (en) 2002-02-26

Family

ID=24245536

Family Applications (1)

Application Number Title Priority Date Filing Date
CA002066278A Expired - Lifetime CA2066278C (en) 1990-08-03 1991-07-25 Computer neural network supervisory process control system and method

Country Status (5)

Country Link
US (1) US5142612A (en)
EP (1) EP0495080B1 (en)
CA (1) CA2066278C (en)
DE (1) DE69128071T2 (en)
WO (1) WO1992002865A1 (en)

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JPH01224804A (en) * 1988-03-04 1989-09-07 Toshiba Corp Ai process monitor and control equipment

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EP0495080B1 (en) 1997-10-29
DE69128071D1 (en) 1997-12-04
WO1992002865A1 (en) 1992-02-20
DE69128071T2 (en) 1998-05-28
US5142612A (en) 1992-08-25
CA2066278C (en) 2002-02-26
EP0495080A1 (en) 1992-07-22

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