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Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
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  • Wiley

More About This Title Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

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Provides comprehensive treatment of the theory of both static and dynamic neural networks.
* Theoretical concepts are illustrated by reference to practical examples Includes end-of-chapter exercises and end-of-chapter exercises.
*An Instructor Support FTP site is available from the Wiley editorial department.
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MADAN M. GUPTA is a professor in the Intelligent Systems Research Laboratory at the University of Saskatchewan, Canada. He received a BE from the Birla Institute of Technology and Science, Pilani, India, and a PhD from the University of Warwick, Canada. A Fellow of the IEEE and the SPIE, Professor Gupta has been awarded the Kaufmann Prize Gold Medal for Research in the field of fuzzy logic.
LIANG JIN received a BS and MSc in electrical engineering from the Changsha Institute of Technology, China, and a PhD in electrical engineering from the Chinese Academy of Space Technology. He is a senior member of the technical staff at Agere Systems in Allentown, Pennsylvania.
NORIYASU HOMMA earned a BA, MA, and PhD in electrical and communication engineering from Tohoku University, Japan, where he is an associate professor. He is currently a visiting professor at the Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Canada.
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Foreword: Lotfi A. Zadeh.

Preface.

Acknowledgments.

PART I: FOUNDATIONS OF NEURAL NETWORKS.

Neural Systems: An Introduction.

Biological Foundations of Neuronal Morphology.

Neural Units: Concepts, Models, and Learning.

PART II: STATIC NEURAL NETWORKS.

Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms.

Advanced Methods for Learning Adaptation in MFNNs.

Radial Basis Function Neural Networks.

Function Approximation Using Feedforward Neural Networks.

PART III: DYNAMIC NEURAL NETWORKS.

Dynamic Neural Units (DNUs): Nonlinear Models and Dynamics.

Continuous-Time Dynamic Neural Networks.

Learning and Adaptation in Dynamic Neural Networks.

Stability of Continuous-Time Dynamic Neural Networks.

Discrete-Time Dynamic Neural Networks and Their Stability.

PART IV: SOME ADVANCED TOPICS IN NEURAL NETWORKS.

Binary Neural Networks.

Feedback Binary Associative Memories.

Fuzzy Sets and Fuzzy Neural Networks.

References and Bibliography.

Appendix A: Current Bibliographic Sources on Neural Networks.

Index.

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