1. Threshold Gates -- 2. Computational Capabilities of Artificial Neural Networks -- 3. Learning Rules -- 4. Mathematical Theory of Neural Learning -- 5. Adaptive Multilayer Neural Networks I -- 6. Adaptive Multilayer Neural Networks II -- 7. Associative Neural Memories -- 8. Global Search Methods for Neural Networks.

As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamental of Artificial Neural Networks, he provides the first systematic account of the artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies that underlie most of the current theory and practice employed by neural network researchers. This text emphasizes the fundamental theoretical aspects of the computational capabilities and the learning abilities of artificial neural networks. The text assumes that the reader is conversant with the concept of a system and the notion of a "state", as well as with the basic elements of Boolean algebra and switching theory.