Fluid and authoritative this well-organized book represents the first comprehensive treatment of neural networks from an engineering perspective providing extensive state-of-the-art coverage that will expose readers to the myriad facets of neural networks and help them appreciate the technology's origin capabilities and potential applications.Examines all the important aspects of this emerging technolgy covering the learning process back propogation radial basis functions recurrent networks self-organizing systems modular networks temporal processing neurodynamics and VLSI implementation. Integrates computer experiments throughout to demonstrate how neural networks are designed and perform in practice. Chapter objectives problems worked examples a bibliography photographs illustrations and a thorough glossary all reinforce concepts throughout. New chapters delve into such areas as support vector machines and reinforcement learning/neurodynamic programming plus readers will find an entire chapter of case studies to illustrate the real-life practical applications of neural networks. A highly detailed bibliography is included for easy reference.For professional engineers and research scientists.
Table of Contents
1. Introduction.
2. Learning Processes.
3. Single-Layer Perceptrons.
4. Multilayer Perceptrons.
5. Radial-Basis Function Networks.
6. Support Vector Machines.
7. Committee Machines.
8. Principal Components Analysis.
9. Self-Organizing Maps.
10. Information-Theoretic Models.
11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.
12. Neurodynamic Programming.
13. Temporal Processing Using Feedforward Networks.
