Principles of artificial neural networks
- Responsibility
- Daniel Graupe.
- Digital
- data file
- Edition
- 2nd ed.
- Imprint
- New Jersey : World Scientific, ©2007.
- Physical description
- 1 online resource (xv, 303 pages) : illustrations
- Series
- Advanced series on circuits and systems ; v. 6.
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Graupe, Daniel.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 291-297) and indexes.
- Contents
-
- Introduction and Role of Artificial Neural Networks
- Fundamentals of Biological Neural Networks
- Basic Principles of ANNs and Their Early Structures
- The Perceptron
- The Madaline
- Back Propagation
- Hopfield Networks
- Counter Propagation
- Adaptive Resonance Theory
- The Cognitron and the Neocogntiron
- Statistical Training
- Recurrent (Time Cycling) Back Propagation Networks
- Large Scale Memory Storage and Retrieval (LAMSTAR) Network.
- (source: Nielsen Book Data)
- Publisher's summary
-
The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.
(source: Nielsen Book Data)
Subjects
Bibliographic information
- Publication date
- 2007
- Series
- Advanced series on circuits and systems ; vol. 6
- ISBN
- 9789812770578 (electronic bk.)
- 9812770577 (electronic bk.)
- 9812706240
- 9789812706249
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