Enhanced descriptions from Syndetics:

Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised. *NEW-New chapters now cover such areas as: - Support vector machines. - Reinforcement learning/neurodynamic programming. - Dynamically driven recurrent networks. *NEW-End-of-chapter problems revised, improved and expanded in number. Detailed solutions manual to accompany the text. *Extensive, state-of-the-art coverage exposes students to the many facets of neural networks and helps them appreciate the technologys capabilities and potential applications. *Detailed analysis of back-propagation learning and multi-layer perceptrons. *Explores the intricacies of the learning process-an essential component for understanding neural networks. *Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics. *Integrates computer experiments throughout, giving students the opportunity to see how neural networks are designed and perform in practice. *Reinforces key concepts w

Table of contents provided by Syndetics

• 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
• 14 Neurodynamics
• 15 Dynamically Driven Recurrent Networks
• Epilogue
• Bibliography
• Index