Quatrième de couverture
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 TO THIS EDITION- 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.
- Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's 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 the opportunity to see how neural networks are designed and perform in practice.
- Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary.
- Includes a detailed and extensive bibliography for easy reference.
- Computer-oriented experiments distributed throughout the book
- Uses Matlab SE version 5.
Présentation de l'éditeur
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
