What Are Fuzzy Expert Systems?

This book mainly introduces the basic concepts and principles of the second-generation expert system-fuzzy expert system. The book is divided into nine chapters. The first chapter describes the basic concepts of the fuzzy expert system and its differences from the traditional expert system. The second and third chapters give the basic processing methods of fuzzy numbers and the use of the T-operator. This is the theoretical basis for the discussion of fuzzy expert systems throughout the book. The fourth to ninth chapters discuss the comprehensive processing of fuzzy data, the various calculation methods of rule matching, the combination of rules or conclusions, the fuzzy implication operator and alternative reasoning. The content of these chapters can be regarded as the first three The application of the theory discussed in chapter.

Fuzzy expert system

Word Count: 210000

Edition: 1

Number of pages: 272

Paper: offset paper

ISBN: 9787560910932

Packaging: Paperback

Categorise Books >> Computer / Network >>

This book can be used as a textbook or reference for senior students, graduate students, such as computer science department, automatic control department, and information engineering department, as well as reference for science and technology workers engaged in artificial intelligence and knowledge engineering research.

Li Fan, male, born in August 1943. Born in Qianjiang City, Hubei Province, Professor of Department of Computer Science and Engineering, Huazhong University of Science and Technology. He graduated from Huazhong Institute of Technology in 1967. Since 1978, he has taught undergraduate and postgraduate students of the Department of Computers "Computer Principles", "Database Technology", "Artificial Intelligence", "Knowledge Engineering and Fuzzy Expert System", etc. Theoretical and applied research on cognitive science, artificial intelligence, neural networks, knowledge engineering, expert systems, approximate (fuzzy) reasoning, non-monotonic reasoning, and common sense reasoning. He is currently undertaking research on projects funded by the National Natural Science Foundation of China. Has published 3 monographs. 1 textbook. Published more than 50 papers at home and abroad.

Chapter One From Traditional Expert System to Fuzzy Expert System

1.1 Traditional Expert System

1.2 Fuzzy Expert System

1.3 Several Major Fuzzy Reasoning Methods

Chapter 2 Fuzzy Numbers in Expert Systems

2.1 Confidence intervals

2.2 Fuzzy numbers

2.3 Addition of Fuzzy Numbers

2.4 Multiplication of Fuzzy Numbers

2.5 Big and Small Operations on Fuzzy Numbers

2.6 Convolution of fuzzy numbers

2.7 L-R fuzzy number

2.8 Triangular fuzzy numbers

2.9 Distance between two fuzzy numbers

Chapter T-Operator and Fuzzy Reasoning

3.1 Definition of T-operator

3.2 Some typical T-counts

3.3 Properties of T-operators

3.4 Fuzzy inference method based on T-operator

3.5 Method of generating T-operator

Chapter 4 Comprehensive Processing of Fuzzy Data

4.1 Synthesis of non-fuzzy data

4.2 Calculation of membership functions

4.3 Language approximation

4.4 Variation calculation

Chapter 5 Matching Calculation of Fuzzy Production Rules

5.1 Truth relationship between antecedent and fact

5.2 Calculation of matching degree

5.3 Comparison with other matching calculation methods

5.4 A few examples

Chapter 6 Calculation Method of Weighted Fuzzy Matching

6.1 Fuzzy pattern matching

6.2 Match calculation

6.3 Treatment of importance

6.4 Handling of variable rights

6.5 Fuzzy partial matching

Chapter 7 Uncertainty Update Calculation

7.1 Expression of uncertainty

7.3 Management of knowledge base

Chapter VIII Combination of Rules or Conclusions

8.1 Overview

8.2 Reasoning using a single rule

8.3 Extended and Reduced Reasoning

8.4 Combination of rules or conclusions

8.5 A practical example

Chapter Nine Fuzzy Implication Operators and Alternative Reasoning

9.1 Fuzzy Implication Operators

9.2 Extension of synthetic reasoning rules

9.3 Generalized selective reasoning

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