Fausett L.V. Fundamentals of Neural Networks

Pearson, 1993, -469 p.There has been a resurgence of interest in artificial neural networks over the last few years, as researchers from diverse backgrounds have produced a firm theoretical foundation and demonstrated numerous applications of this rich field of study. However, the interdisciplinary nature of neural networks complicates the development of a comprehensive, but introductory, treatise on the subject. Neural networks are useful tools for solving many types of problems. These problems may be characterized as mapping (including pattern association and pattern classification), clustering, and constrained optimization. There are several neural networks available for each type of problem. In order to use these tools effectively it is important to understand the characteristics (strengths and limitations) of each.
This book presents a wide variety of standard neural networks, with diagrams of the architecture, detailed statements of the training algorithm, and several examples of the application for each net. In keeping with our intent to show neural networks in a fair but objective light, typical results of simple experiments are included (rather than the best possible). The emphasis is on computational characteristics, rather than psychological interpretations. TO illustrate the similarities and differences among the neural networks discussed, similar examples are used wherever it is appropriate.
Fundamentals of Neural Networks has been written for students and for researchers in academia, industry, and govemment who are interested in using neural networks. It has been developed both as a textbook for a one semester, or two quarter, Introduction to Neural Networks course at Florida Institute of Technology, and as a resource book for researchers. Our course has been developed jointly by neural networks researchers from applied mathematics, comxiii puter science, and computer and electrical engineering. Our students are seniors, or graduate students, in science and engineering; many work in local industry.Simple Neural Nets for Pattern Classification
Pattern Association
Neural Networks Based on Competition
Adaptive Resonance Theory
Backpropagation Neural Net
A Sampler of Other Neural Nets