Static and Dynamic Neural Networks - From Fundamentals to Advanced Theory

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.

Contents

• Neural Systems: An Introduction
• Biological Foundations of Neuronal Morphology
• Neural Units: Concepts, Models, and Learning

• 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

• 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

• Binary Neural Networks
• Feedback Binary Associative Memories
• Fuzzy Sets and Fuzzy Neural Networks