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Hybrid Neural Network and Expert Systems presents the basics of expert systems and neural networks, and the important characteristics relevant to the integration of these two technologies. Through case studies of actual working systems, the author demonstrates the use of these hybrid systems in practical situations. Guidelines and models are described to help those who want to develop their own hybrid systems.
Neural networks and expert systems together represent two major aspects of human intelligence and therefore are appropriate for integration. Neural networks represent the visual, pattern-recognition types of intelligence, while expert systems represent the logical, reasoning processes. Together, these technologies allow applications to be developed that are more powerful than when each technique is used individually.
Hybrid Neural Network and Expert Systems provides frameworks for understanding how the combination of neural networks and expert systems can produce useful hybrid systems, and illustrates the issues and opportunities in this dynamic field.


Hybrid Neural Network and Expert Systems presents the basics of expert systems and neural networks, and the important characteristics relevant to the integration of these two technologies. Through case studies of actual working systems, the author demonstrates the use of these hybrid systems in practical situations. Guidelines and models are described to help those who want to develop their own hybrid systems.
Neural networks and expert systems together represent two major aspects of human intelligence and therefore are appropriate for integration. Neural networks represent the visual, pattern-recognition types of intelligence, while expert systems represent the logical, reasoning processes. Together, these technologies allow applications to be developed that are more powerful than when each technique is used individually.
Hybrid Neural Network and Expert Systems provides frameworks for understanding how the combination of neural networks and expert systems can produce useful hybrid systems, and illustrates the issues and opportunities in this dynamic field.

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Preface. Part I: Fundamentals of Hybrid Systems. 1. Overview of Neural and Symbolic Systems. 2. Research in Hybrid Neural and Symbolic Systems. 3. Models for Integrating Systems. Part II: Case Studies of Hybrid Neural Network and Expert Systems. 4. LAM Hybrid System for Window Glazing Design. 5. Hybrid Systems Approach to Nuclear Plant Monitoring. 6. Chemical Tank Control System. 7. Image Interpretation via Fusion of Heterogeneous Sources using a Hybrid Expert-Neural Network System. 8. Hybrid Systems for Multiple Target Recognition. Part III: Analysis and Guidelines. 9. Guidelines for Developing Hybrid Systems. 10. Tools and Development Systems. 11. Summary and the Future of Hybrid Neural Network and Expert Systems. References. Index.

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