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CA2066159A1 - On-line training neural network for process control - Google Patents

On-line training neural network for process control

Info

Publication number
CA2066159A1
CA2066159A1 CA 2066159 CA2066159A CA2066159A1 CA 2066159 A1 CA2066159 A1 CA 2066159A1 CA 2066159 CA2066159 CA 2066159 CA 2066159 A CA2066159 A CA 2066159A CA 2066159 A1 CA2066159 A1 CA 2066159A1
Authority
CA
Grant status
Application
Patent type
Prior art keywords
training
data
neural
network
sets
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
CA 2066159
Other languages
French (fr)
Other versions
CA2066159C (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 Inc
Original Assignee
E I du Pont de Nemours and Co
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

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Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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
    • G05B13/027Adaptive 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
    • YGENERAL 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
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00Data processing: artificial intelligence
    • Y10S706/902Application using ai with detail of the ai system
    • Y10S706/903Control
    • Y10S706/906Process plant

Abstract

An on-line training neural network for pro-cess control system and method trains by retrieving training sets from the stream of process data. The neural network detects the availability of new train-ing data, and constructs a training set by retrieving the corresponding input data. The neural network is trained using the training set. Over time, many training sets are presented to the neural network.
When multiple presentations are needed to effec-tively train, a buffer of training sets is filled and up-date as new training data becomes available. The size of the buffer is selected in accordance with the training needs of the neural network. Once the buf-fer is full, a new training set bumps the oldest train-ing set off the top of the buffer stack. The training sets in the buffer stack can be presented one or more times each time a new training set is con-structed. A historical database of timestamped data can be used to construct training sets when training input data has a time delay from sample time to availability for the neural network. The timestamps of the training input data are used to select the ap-propriate timestamp at which input data is re-trieved for use in the training set. Using the histori-cal database, the neural network can be trained ret-rospectively by searching the historical database and constructing training sets based on past data.
CA 2066159 1990-08-03 1991-07-25 On-line training neural network for process control Expired - Lifetime CA2066159C (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US07563092 US5212765A (en) 1990-08-03 1990-08-03 On-line training neural network system for process control
US07/563,092 1990-08-03
PCT/US1991/005260 WO1992002867A1 (en) 1990-08-03 1991-07-25 On-line training neural network for process control

Publications (2)

Publication Number Publication Date
CA2066159A1 true true CA2066159A1 (en) 1992-02-04
CA2066159C CA2066159C (en) 2003-04-22

Family

ID=24249081

Family Applications (1)

Application Number Title Priority Date Filing Date
CA 2066159 Expired - Lifetime CA2066159C (en) 1990-08-03 1991-07-25 On-line training neural network for process control

Country Status (5)

Country Link
US (4) US5212765A (en)
EP (1) EP0495046B1 (en)
CA (1) CA2066159C (en)
DE (2) DE69125809D1 (en)
WO (1) WO1992002867A1 (en)

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US5640493A (en) 1997-06-17 grant
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