Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering


Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.

In a clear and accessible style, Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.

Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material.

Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Table of Contents

  1. Foreword
  2. Preface
  3. 1. The Faculty of Knowledge Engineering and Problem Solving
  4. 2. Knowledge Engineering and Symbolic Artificial Intelligence
  5. 3. From Fuzzy Sets to Fuzzy Systems
  6. 4. Neural Networks: Theoretical and Computational Models
  7. 5. Neural Networks for Knowledge Engineering and Problem Solving
  8. 6. Hybrid Symbolic, Fuzzy, and Connectionist Systems: Toward Comprehensive Artificial Intelligence
  9. 7. Neural Networks, Fuzzy Systems, and Nonlinear Dynamical Systems. Chaos; Toward New Connectionist and Fuzzy Logic Models
  10. Appendixes
  11. References
  12. Glossary
  13. Index