Fuzzy Set Theory—and Its ApplicationsSince its inception, the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of fuzzy technology can be found in artificial intelligence, computer science, control engineering, decision theory, expert systems, logic, management science, operations research, robotics, and others. Theoretical advances have been made in many directions. The primary goal of Fuzzy Set Theory - and its Applications, Fourth Edition is to provide a textbook for courses in fuzzy set theory, and a book that can be used as an introduction. To balance the character of a textbook with the dynamic nature of this research, many useful references have been added to develop a deeper understanding for the interested reader. Fuzzy Set Theory - and its Applications, Fourth Edition updates the research agenda with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. Chapters have been updated and extended exercises are included. |
From inside the book
Page vi
... Crisp Interval 100 FFRR88 88888 71 71 76 79 83 86 93 93 95 99 7.3.2 Integration of a ( Crisp ) Real - Valued Function over a Fuzzy Interval 103 7.4 Fuzzy Differentiation 107 8 Uncertainty Modeling 111 8.1 Application - oriented Modeling ...
... Crisp Interval 100 FFRR88 88888 71 71 76 79 83 86 93 93 95 99 7.3.2 Integration of a ( Crisp ) Real - Valued Function over a Fuzzy Interval 103 7.4 Fuzzy Differentiation 107 8 Uncertainty Modeling 111 8.1 Application - oriented Modeling ...
Page vii
... Crisp Databases 268 13 Fuzzy Data Analysis 277 13.1 Introduction 277 13.2 Methods for Fuzzy Data Analysis 279 13.2.1 Algorithmic Approaches 281 13.2.2 Knowledge - Based Approaches 302 13.2.3 Neural Net Approaches 304 13.3 Dynamic Fuzzy ...
... Crisp Databases 268 13 Fuzzy Data Analysis 277 13.1 Introduction 277 13.2 Methods for Fuzzy Data Analysis 279 13.2.1 Algorithmic Approaches 281 13.2.2 Knowledge - Based Approaches 302 13.2.3 Neural Net Approaches 304 13.3 Dynamic Fuzzy ...
Page viii
... Crisp Objective Function 342 14.3 Fuzzy Dynamic Programming 348 14.3.1 Fuzzy Dynamic Programming with Crisp State Transformation Function 349 14.4 Fuzzy Multicriteria Analysis 352 14.4.1 Multi Objective Decision Making ( MODM ) 353 14.4 ...
... Crisp Objective Function 342 14.3 Fuzzy Dynamic Programming 348 14.3.1 Fuzzy Dynamic Programming with Crisp State Transformation Function 349 14.4 Fuzzy Multicriteria Analysis 352 14.4.1 Multi Objective Decision Making ( MODM ) 353 14.4 ...
Page xv
... crisp sets relates to the combination of fuzzy sets through disjunc- tion and conjunction or , equivalently , union and intersection . Professor Zimmer- mann and his associates at the Technical University of Aachen have made many ...
... crisp sets relates to the combination of fuzzy sets through disjunc- tion and conjunction or , equivalently , union and intersection . Professor Zimmer- mann and his associates at the Technical University of Aachen have made many ...
Page 1
... crisp , deterministic , and precise in character . By crisp we mean dichotomous , that is , yes - or - no - type rather than more - or - less type . In conventional dual logic , for instance , a statement can be true or false — and ...
... crisp , deterministic , and precise in character . By crisp we mean dichotomous , that is , yes - or - no - type rather than more - or - less type . In conventional dual logic , for instance , a statement can be true or false — and ...
Contents
1 | |
8 | |
22 | |
4 | 44 |
The Extension Principle and Applications | 54 |
Fuzzy Relations on Sets and Fuzzy Sets | 71 |
3 | 82 |
7 | 88 |
Applications of Fuzzy Set Theory | 139 |
3 | 154 |
4 | 160 |
5 | 169 |
Fuzzy Sets and Expert Systems | 185 |
Fuzzy Control | 223 |
Fuzzy Data Bases and Queries | 265 |
Decision Making in Fuzzy Environments | 329 |
3 | 95 |
4 | 105 |
2 | 122 |
4 | 131 |
Applications of Fuzzy Sets in Engineering and Management | 371 |
Empirical Research in Fuzzy Set Theory | 443 |
Future Perspectives | 477 |
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Common terms and phrases
a-level aggregation algebraic algorithm applications of fuzzy approach approximately areas base basic Bezdek chapter classical computational concepts considered constraints crisp criteria customers data analysis DataEngine decision defined definition defuzzification degree of membership described determine domain Dubois and Prade elements engineering example expert systems feature formal Fril fuzzy c-means fuzzy clustering fuzzy control fuzzy control systems fuzzy function fuzzy graph fuzzy logic fuzzy measures fuzzy numbers fuzzy relation fuzzy set Ć fuzzy set theory goal inference inference engine input integral intersection interval linear programming linguistic variable Mamdani mathematical measure of fuzziness membership function methods min-operator objective function operators optimal parameters possibility distribution probability probability theory problem properties respect rules scale level scheduling semantic solution structure Sugeno t-conorms t-norms Table tion trajectories truth tables truth values uncertainty vector x₁ Yager Zadeh Zimmermann µĆ(x µµ(x