Engineering

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.

NEW TO THIS EDITION

• NEW - New chapters now cover such areas as:

  • Support vector machines. Pg.___

  • Reinforcement learning/neurodynamic programming. Pg.___

  • Dynamically driven recurrent networks. Pg.___

• NEW - End-of-chapter problems revised, improved and expanded in number. Detailed solutions manual to accompany the text. Pg.___

• Extensive, state-of-the-art coverage exposes students to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications. Pg.___

• Detailed analysis of back-propagation learning and multi-layer perceptrons. Pg.___

• Explores the intricacies of the learning process—an essential component for understanding neural networks. Pg.___

• 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. Pg.___

• Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary. Pg.___

• Includes a detailed and extensive bibliography for easy reference. Pg.___

• Computer-oriented experiments distributed throughout the text.

• Uses Matlab SE version 5.

• New chapters now cover such areas as:

  • Support vector machines. Pg.___

  • Reinforcement learning/neurodynamic programming. Pg.___

  • Dynamically driven recurrent networks. Pg.___

• End-of-chapter problems revised, improved and expanded in number. Detailed solutions manual to accompany the text. Pg.___

 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.

Fuzzy Systems/Control [FOUNDATIONS OF ENGINEERING] (Electrical and Computing Engineering)
Neural Networks and Fuzzy Systems (Computer Science)

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.

 

Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

• Neural Networks and Learning Machines, 3/E
  Haykin
  © 2009 | Prentice Hall | Cloth; 936 pages | Instock
  ISBN-10: 0131471392 | ISBN-13: 9780131471399
  Brief Description | Buy from myPearsonStore
  

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.
FEATURES
• 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.