NEURAL NETWORKS FOR IDENTIFICATION, PREDICTION AND CONTROL

Pham

ISBN 10: 3540199594 / ISBN 13: 9783540199595

Verlag: Springer, 1900

Beschreibung

NEURAL NETWORKS FOR IDENTIFICATION, PREDICTION AND CONTROL | Pham | Springer, 1995. In-8° cartonné, 238 pages. Couverture propre . Dos solide. Intérieur frais sans soulignage ou annotation. Exemplaire de bibliothèque : petit code barre en pied de 1re de couv., cotation au dos, rares et discrets petits tampons à l'intérieur de l'ouvrage. Très bon état général pour cet ouvrage. [Ba 56+]. Bestandsnummer des Verkäufers YA-ZKAM-XSU5

Bibliografische Details

Titel: NEURAL NETWORKS FOR IDENTIFICATION, ...

Verlag: Springer

Erscheinungsdatum: 1900

Einband: Hardcover

Zustand: Très bon

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.