by Milan Hajek
Publisher: University of KwaZulu-Natal 2005
Number of pages: 114
Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).
This document is no more available for free.
by Ivan F Wilde - King's College London
These notes are based on lectures given in the Mathematics Department at King's College London. An attempt has been made to present a logical (mathematical) account of some of the basic ideas of the 'artificial intelligence' aspects of the subject.
by Martin T. Hagan, et al.
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications.
by Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
by Raul Rojas - Springer
A general theory of artificial neural nets. The book starts with the simple nets, and shows how the models change when more general computing elements and net topologies are introduced. Suitable as a basis for university courses in neurocomputing.