Overview

This work 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 visual and 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 field.

Full Product Details

Author:   Larry R. Medsker
Publisher:   Springer
Imprint:   Springer
Edition:   1994 ed.
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   1.190kg
ISBN:  

9780792394235

ISBN 10:   0792394232
Pages:   240
Publication Date:   31 December 1993
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

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.- 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 System for Multiple Target Recognition.- 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.

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