This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.

This monograph provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the computationally intensive methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling. The reader is provided with the information needed to make practical use of the powerful modeling and design tool of feedforward neural networks, as well as presented with the background needed to make contributions to several research frontiers. This work is therefore of interest to those in electrical engineering, operations research, computer science, and statistics who would like to use nonlinear modeling of stochastic phenomena to treat problems of pattern classification, forecasting, signal processing, machine intelligence, and nonlinear regression. T.L. Fine is Professor of Electrical Engineering at Cornell University.