Static and dynamic neural networks : from fundamentals to advanced theory /
Madan M. Gupta, Liang Jin, Noriyasu Homma.
- 1 PDF (xxviii, 722 pages) : illustrations.
Includes bibliographical references and index.
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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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.