Neural Networks for Identification, Prediction and Control

PHAM, P. T.; LIU, X.

ISBN 10: 3540199594 / ISBN 13: 9783540199595

Verlag: Springer, London, 1995

Beschreibung

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

Bestandsnummer des Verkäufers 116450

Bibliografische Details

• Titel: Neural Networks for Identification, Prediction and Control
• Verlag: Springer, London
• Erscheinungsjahr: 1995
• ISBN-10: 3540199594
• ISBN-13: 9783540199595
• Einband: Hardcover
• Zustand: As new
• Verkäuferkataloge:
  • Science

Über diesen Titel

Inhaltsangabe

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.

Reseña 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.