Principles of artificial neural networks / Daniel Graupe.

Format:

Book

Author/Creator:

Graupe, Daniel.

Series:

Advanced series on circuits and systems ; v. 6.

Advanced series on circuits and systems ; vol. 6

Language:

English

Subjects (All):

Neural networks (Computer science).

Physical Description:

1 online resource (320 p.)

Edition:

2nd ed.

Place of Publication:

Singapore ; Hackensack, N.J. : World Scientific, c2007.

Language Note:

English

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 strength

Contents:

Acknowledgments; Preface to the First Edition; Preface to the Second Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; Chapter 4. The Perceptron; Chapter 5. The Madaline; Chapter 6. Back Propagation; Chapter 7. Hopeld Networks; Chapter 8. Counter Propagation; Chapter 9. Adaptive Resonance Theory; Chapter 10. The Cognitron and the Neocognitron; Chapter 11. Statistical Training; Chapter 12. Recurrent (Time Cycling) Back Propagation Networks

Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) Network Problems; References; Author Index; Subject Index

Notes:

Description based upon print version of record.

Includes bibliographical references (p. 291-297) and indexes.

ISBN:

9786611121709

9781281121707

1281121703

9789812770578

9812770577

OCLC:

648316923