Neural Networks for Identification, Prediction and Control

PHAM, P. T.; LIU, X.

ISBN 10: 3540199594 ISBN 13: 9783540199595

Editorial: Springer, London, 1995

Condici�n: Usado - Como Nuevo Encuadernaci�n de tapa dura

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Descripci�n

xiv, 238 p. 24 cm. 98 Figures and tables. Hardcover.

N� de ref. del art�culo 116450

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Detalles bibliogr�ficos

T�tulo

Neural Networks for Identification, Prediction and Control

Editor

Springer, London

Fecha de publicaci�n

1995

ISBN 10

3540199594

ISBN 13

9783540199595

Encuadernaci�n

Hardcover

Estado

As new

Cat�logos de vendedores

Science

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Sinopsis

This publication describes examples of applications of neural networks in modelling, prediction and control. Topics covered include identification of general linear and nonlinear processes, forecasting of river levels, stock market prices, currency exchange rates and control of a time-delayed plant and a two-joint robot. The neural network types considered are the multilayer perceptron (MLP), the Elman and Jordan networks, the Group-Method-of-Data Handling (GMDH), the cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems. The algorithms presented are the standard backpropagation (BP) algorithm, the Widrow-Hoff learning, dynamic BP and evolutionary learning. Full listings of computer programmes written in C for neural-network-based system identification and prediction to facilitate practical experimentation with neural network techniques are included.

Rese�a del editor

This publication describes examples of applications of neural networks in modelling, prediction and control. Topics covered include identification of general linear and nonlinear processes, forecasting of river levels, stock market prices, currency exchange rates, and control of a time-delayed plant and a two-joint robot. The neural network types considered are the multilayer perceptron (MLP), the Elman and Jordan networks, the Group-Method-of-Data-Handling (GMDH), the cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems. The algorithms presented are the standard backpropagation (BP) algorithm, the Widrow-Hoff learning, dynamic BP and evolutionary learning. Full listings of computer programs written in C for neural-network-based system identification and prediction to facilitate practical experimentation with neural network techniques are included.

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