Description
Book SynopsisProvides 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.
Table of ContentsForeword: 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.
