CA2105842A1 - Neural Network Based Control System - Google Patents

Neural Network Based Control System

Info

Publication number
CA2105842A1
CA2105842A1 CA2105842A CA2105842A CA2105842A1 CA 2105842 A1 CA2105842 A1 CA 2105842A1 CA 2105842 A CA2105842 A CA 2105842A CA 2105842 A CA2105842 A CA 2105842A CA 2105842 A1 CA2105842 A1 CA 2105842A1
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CA
Canada
Prior art keywords
local
neural network
data
inputs
model
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
CA2105842A
Other languages
French (fr)
Other versions
CA2105842C (en
Inventor
James William Hall
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.)
Deere and Co
Original Assignee
James William Hall
Deere & Company
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
Application filed by James William Hall, Deere & Company filed Critical James William Hall
Publication of CA2105842A1 publication Critical patent/CA2105842A1/en
Application granted granted Critical
Publication of CA2105842C publication Critical patent/CA2105842C/en
Anticipated expiration legal-status Critical
Expired - Fee Related 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
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01D—HARVESTING; MOWING
    • A01D41/00—Combines, i.e. harvesters or mowers combined with threshing devices
    • A01D41/12—Details of combines
    • A01D41/127—Control or measuring arrangements specially adapted for combines

Abstract

A neural network is trained with a general set of data to function as a general model of a machine or process with local condition inputs set equal to zero. The network is then retrained or receives additional training on an extentd data set containing the general set of data, characterized by zero values for the local condition inputs, and data on specific local conditions, characterized by non-zero values for the local condition inputs. The result is a trained neural network which functions as a general model when the inputs for the local conditions inputs are set equal to zero, and which functions as a model of some specific local condition when the local condition inputs match the encoding of the some local data set contained within the training data. The neural network has an architecture and a number of neurons such that its functioning as the local model is partially dependent upon its functioning as the general model. This trained neural network is combined with sensors, actuators, a control and communications computer and with a user interface to function as combine control system.
CA002105842A 1992-09-10 1993-09-09 Neural network based control system Expired - Fee Related CA2105842C (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US94529392A 1992-09-10 1992-09-10
US945,293 1992-09-10

Publications (2)

Publication Number Publication Date
CA2105842A1 true CA2105842A1 (en) 1994-03-11
CA2105842C CA2105842C (en) 1998-11-03

Family

ID=25482924

Family Applications (1)

Application Number Title Priority Date Filing Date
CA002105842A Expired - Fee Related CA2105842C (en) 1992-09-10 1993-09-09 Neural network based control system

Country Status (6)

Country Link
US (1) US5586033A (en)
EP (1) EP0586999B1 (en)
JP (1) JP3478855B2 (en)
AU (1) AU658066B2 (en)
CA (1) CA2105842C (en)
DE (1) DE69323943T2 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111802061A (en) * 2019-04-10 2020-10-23 迪尔公司 Machine control using real-time models

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Also Published As

Publication number Publication date
AU658066B2 (en) 1995-03-30
JP3478855B2 (en) 2003-12-15
EP0586999A2 (en) 1994-03-16
US5586033A (en) 1996-12-17
DE69323943D1 (en) 1999-04-22
EP0586999A3 (en) 1995-03-29
JPH06176179A (en) 1994-06-24
DE69323943T2 (en) 1999-08-12
AU3993093A (en) 1994-03-17
EP0586999B1 (en) 1999-03-17
CA2105842C (en) 1998-11-03

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