Rojas R. Neural Networks. A Systematic Introduction

Springer, 1996. — 512 p.This book arose from my lectures on neural networks at the Free University of Berlin and later at the University of Halle. I started writing a new text out of dissatisfaction with the literature available at the time. Most books on neural networks seemed to be chaotic collections of models and there was no clear unifying theoretical thread connecting them. The results of my efforts were published in German by Springer-Verlag under the title Theorie der neuronalen Netze. I tried in that book to put the accent on a systematic development of neural network theory and to stimulate the intuition of the reader by making use of many figures. Intuitive understanding fosters a more immediate grasp of the objects one studies, which stresses the concrete meaning of their relations. Since then some new books have appeared, which are more systematic and comprehensive than those previously available, but I think that there is still much room for improvement. The German edition has been quite successful and at the time of this writing it has gone through five printings in the space of three years.
However, this book is not a translation. I rewrote the text, added new sections, and deleted some others. The chapter on fast learning algorithms is completely new and some others have been adapted to deal with interesting additional topics. The book has been written for undergraduates, and the only mathematical tools needed are those which are learned during the first two years at university. The book offers enough material for a semester, although I do not normally go through all chapters. It is possible to omit some of them so as to spend more time on others. Some chapters from this book have been used successfully for university 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 Backpropagation 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