Neural networks

A systematic introduction

Raul Rojas

522 pages, parution le 29/04/1996

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Résumé

Artificial neural networks are an alternative computational paradigm with roots in neurobiology which has attracted increasing interest in recent years. This book is a comprehensive introduction to the topic that stresses the systematic development of the underlying theory. Starting from simple threshold elements, more advanced topics are introduced, such as multilayer networks, efficient learning methods, recurrent networks, and self-organization. The various branches of neural network theory are interrelated closely and quite often unexpectedly, so the chapters treat the underlying connection between neural models and offer a unified view of the current state of research in the field.

The book has been written for anyone interested in understanding artificial neural networks or in learning more about them. The only mathematical tools needed are those learned during the first two years at university. The text contains more than 300 figures to stimulate the intuition of the reader and to illustrate the kinds of computation performed by neural networks. Material from the book has been used successfully for courses in Germany, Austria and the United States.

The biological paradigm
Threshold logic
Weighted networks - The perceptron
Perceptron learning
Unsupervised learning and clustering algorithms
One and two layered networks
The back propagation algorithm
Fast learning algorithms
Statistics and neural networks
The complexity of learning
Fuzzy logic
Associative networks
The hopfield model
Stochastic networks
Kohonen networks
Modular neural networks
Genetic algorithms
Hardware for neural networks

L'auteur - Raul Rojas

University of Halle, Germany.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Raul Rojas
Parution	29/04/1996
Nb. de pages	522
Format	15,5 x 23,5
Couverture	Broché
Poids	780g
Intérieur	Noir et Blanc
EAN13	9783540605058