Fundamentals of Artificial Neural Networks

This book uses tools from nonlinear systems theory to provide a comprehensive foundation for the theory of neural networks. The emphasis is on computational capabilities and learning abilities of neural networks. The unified perspective of nonlinear systems leads to a clear understanding of various architectures and learning methods, and the two chapters on learning provide valuable insight. In addition to the most common feed-forward networks, the book analyzes radial basis function networks, classifier networks, clustering networks, and various models of associative memory. The book is intended to be used for a first-year graduate course. The required background includes basic topics in mathematics, such as probability and statistics, differential equations, linear algebra, multivariate calculus, as well as some knowledge of state systems, Boolean algebra, and switching theory.