This book gives a systemic account of major concepts, methodologies of artificial neural networks and to present a unified frame work that makes the subject more accessible to students and practitioners. The book emphasizes fundamental theoretical aspects of the computational capabilities and learning abilities of artificial neural networks. It integrates important theoretical results on artificial neural networks and uses them to explain a wide range of existing empirical observations and commonly used heuristics. The main audience of the book is undergraduate students in electrical engineering, computer science and engineering. It can also be used as a valuable resource for practical engineering, computer scientists and others involved in research of artificial neural networks.
1. Overview of Neural Networks, 2. Fundamentals of Neural Networks, 3. Feed forward Neural Networks, 4. Neural Networks Architectures, 5. Associative Memories, 6. Introduction to Fuzzy Sets: Basic Definitions and Relations, 7. Introduction to Fuzzy Logic, 8. Fuzzy Control and Stability, 8A. Advanced Process Control, 8B. Fuzzy Logic Application