ES2102365T3 - Aparato y procedimiento para controlar un proceso utilizando una red adiestrada de procesado distribuido paralelo. - Google Patents

Aparato y procedimiento para controlar un proceso utilizando una red adiestrada de procesado distribuido paralelo.

Info

Publication number
ES2102365T3
ES2102365T3 ES90904112T ES90904112T ES2102365T3 ES 2102365 T3 ES2102365 T3 ES 2102365T3 ES 90904112 T ES90904112 T ES 90904112T ES 90904112 T ES90904112 T ES 90904112T ES 2102365 T3 ES2102365 T3 ES 2102365T3
Authority
ES
Spain
Prior art keywords
network
error
distributed processing
outputs
representative
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.)
Expired - Lifetime
Application number
ES90904112T
Other languages
English (en)
Inventor
Aaron James Owens
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.)
EIDP Inc
Original Assignee
EI 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
Application filed by EI Du Pont de Nemours and Co filed Critical EI Du Pont de Nemours and Co
Application granted granted Critical
Publication of ES2102365T3 publication Critical patent/ES2102365T3/es
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

Landscapes

  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Multi Processors (AREA)
  • Feedback Control In General (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Devices For Executing Special Programs (AREA)
  • Control By Computers (AREA)
  • General Factory Administration (AREA)
  • Electrotherapy Devices (AREA)
  • Electrical Discharge Machining, Electrochemical Machining, And Combined Machining (AREA)
  • Preparation Of Compounds By Using Micro-Organisms (AREA)

Abstract

UNA RED DE PROCESO DISTRIBUIDO EN PARALELO (14) ANTERIORMENTE ENSEÑADA PARA SIMULAR UN PROCESO, RESPONDE A ACTIVACIONES DE DATOS DE ENTRADA A (42) PARA PRODUCIR DATOS DE SALIDA Y (40). LAS ACTIVACIONES SON REPRESENTATIVAS DE LOS PARAMETROS DE ENTRADA P (16) DEL PROCESO (12) Y LOS DE SALIDA SON REPRESENTATIVOS DE LAS CARACTERISTICAS C (18) DEL PRODUCTO DEL PROCESO. UN CONJUNTO DE DATOS DE OBJETIVOS (26) CORRESPONDIENTES A LAS CARACTERISTICAS FISICAS DESEADAS D (20) DEL PRODUCTO SON PRODUCIDAS Y UTILIZADAS PARA GENERAR SEÑALES REPRESENTATIVAS DE ERROR E ENTRE LA SEÑAL DE OBJETIVOS G (26) Y LOS DATOS SALIDAS Y (40). SE DETERMINAN LOS VALORES DE LOS DATOS ACTUALIZADOS AU (44) DE LA RED (14) QUE SON NECESARIOS PARA CONDUCIR AL ERROR E (38) A UN MINIMO PREDETERMINADO, Y LOS PARAMETROS REALES FISICOS DE ENTRADA P (16) DEL PROCESO SON ALTERADOS PARA QUE SE CORRESPONDAN CON LOS VALORES DE ENTRADA AU (44), PRODUCIENDOSE UN ERROR MINIMO. ESTA INVENCION PUEDE ACTUALIZARSE EN UNA SITUACION DE UTILIZACION POR LOTES O UNA SITUACION DE PROCESO DE CONTROL SOBRE LA MARCHA.
ES90904112T 1989-02-28 1990-02-28 Aparato y procedimiento para controlar un proceso utilizando una red adiestrada de procesado distribuido paralelo. Expired - Lifetime ES2102365T3 (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US31671789A 1989-02-28 1989-02-28

Publications (1)

Publication Number Publication Date
ES2102365T3 true ES2102365T3 (es) 1997-08-01

Family

ID=23230334

Family Applications (1)

Application Number Title Priority Date Filing Date
ES90904112T Expired - Lifetime ES2102365T3 (es) 1989-02-28 1990-02-28 Aparato y procedimiento para controlar un proceso utilizando una red adiestrada de procesado distribuido paralelo.

Country Status (9)

Country Link
EP (1) EP0461158B1 (es)
JP (1) JPH04503724A (es)
AT (1) ATE153788T1 (es)
CA (1) CA2011135C (es)
DE (1) DE69030812T2 (es)
DK (1) DK0461158T3 (es)
ES (1) ES2102365T3 (es)
IL (1) IL93522A0 (es)
WO (1) WO1990010270A1 (es)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3242950B2 (ja) * 1991-08-14 2001-12-25 株式会社東芝 予測制御方法
US5566275A (en) * 1991-08-14 1996-10-15 Kabushiki Kaisha Toshiba Control method and apparatus using two neural networks
DE4131765A1 (de) * 1991-09-24 1993-03-25 Siemens Ag Regelparameter-verbesserungsverfahren fuer industrielle anlagen
US5353207A (en) * 1992-06-10 1994-10-04 Pavilion Technologies, Inc. Residual activation neural network
US5825646A (en) 1993-03-02 1998-10-20 Pavilion Technologies, Inc. Method and apparatus for determining the sensitivity of inputs to a neural network on output parameters
EP0687369A1 (en) * 1993-03-02 1995-12-20 Pavilion Technologies Inc. Method and apparatus for analyzing a neural network within desired operating parameter constraints
DE4416364B4 (de) * 1993-05-17 2004-10-28 Siemens Ag Verfahren und Regeleinrichtung zur Regelung eines Prozesses
US6363289B1 (en) 1996-09-23 2002-03-26 Pavilion Technologies, Inc. Residual activation neural network
JP2002543408A (ja) * 1999-04-30 2002-12-17 クウオリコン インコーポレーテッド Ｄｎａ融解曲線分析に基づくアッセイ
US8489529B2 (en) * 2011-03-31 2013-07-16 Microsoft Corporation Deep convex network with joint use of nonlinear random projection, Restricted Boltzmann Machine and batch-based parallelizable optimization

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3221151A (en) * 1962-01-22 1965-11-30 Massachusetts Inst Technology Adaptive control system using a performance reference model
FR2423806A1 (fr) * 1977-05-26 1979-11-16 Anvar Procede de regulation a modele de reference et regulateur mettant en oeuvre ce procede
US4578747A (en) * 1983-10-14 1986-03-25 Ford Motor Company Selective parametric self-calibrating control system

Also Published As

Publication number Publication date
DE69030812D1 (de) 1997-07-03
IL93522A0 (en) 1990-11-29
EP0461158B1 (en) 1997-05-28
DE69030812T2 (de) 1997-10-16
ATE153788T1 (de) 1997-06-15
DK0461158T3 (da) 1997-07-14
JPH04503724A (ja) 1992-07-02
CA2011135C (en) 2001-03-13
CA2011135A1 (en) 1990-08-31
WO1990010270A1 (en) 1990-09-07
EP0461158A4 (en) 1992-08-12
EP0461158A1 (en) 1991-12-18

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