This book provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Such a systematic and unified treatment, makes the subject more accessible to students and practitioners. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references.

Preface
Acknowledgments
Abbreviations
Symbols

Threshold Gates
Computational Capabilities of Artificial Networks
Leanring Rules
Mathematical Theory of Neural Learning
Adaptive Multilayer Neural Networks
Adaptive Multilayer Neural Networks II
Associative Neural Memories
Global Search Methods for Neural Networks
References
Index