Static & Dynamic Neural Networks – From Fundamentals to Advanced Theory
From Fundamentals to Advanced Theory
Samenvatting
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.
Specificaties
Inhoudsopgave
<p>Preface.</p>
<p>Acknowledgments.</p>
<p>PART I: FOUNDATIONS OF NEURAL NETWORKS.</p>
<p>Neural Systems: An Introduction.</p>
<p>Biological Foundations of Neuronal Morphology.</p>
<p>Neural Units: Concepts, Models, and Learning.</p>
<p>PART II: STATIC NEURAL NETWORKS.</p>
<p>Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms.</p>
<p>Advanced Methods for Learning Adaptation in MFNNs.</p>
<p>Radial Basis Function Neural Networks.</p>
<p>Function Approximation Using Feedforward Neural Networks.</p>
<p>PART III: DYNAMIC NEURAL NETWORKS.</p>
<p>Dynamic Neural Units (DNUs): Nonlinear Models and Dynamics.</p>
<p>Continuous–Time Dynamic Neural Networks.</p>
<p>Learning and Adaptation in Dynamic Neural Networks.</p>
<p>Stability of Continuous–Time Dynamic Neural Networks.</p>
<p>Discrete–Time Dynamic Neural Networks and Their Stability.</p>
<p>PART IV: SOME ADVANCED TOPICS IN NEURAL NETWORKS.</p>
<p>Binary Neural Networks.</p>
<p>Feedback Binary Associative Memories.</p>
<p>Fuzzy Sets and Fuzzy Neural Networks.</p>
<p>References and Bibliography.</p>
<p>Appendix A: Current Bibliographic Sources on Neural Networks.</p>
<p>Index.</p>