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
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Page vi
... Reasoning Linguistic Variables 141 141 9.2 Fuzzy Logic 149 9.2.1 Classical Logics Revisited 149 9.2.2 Linguistic Truth Tables 153 9.3 Approximate and Plausible Reasoning 156 9.4 Fuzzy Languages 160 9.5 Support Logic Programming and Fril ...
... Reasoning Linguistic Variables 141 141 9.2 Fuzzy Logic 149 9.2.1 Classical Logics Revisited 149 9.2.2 Linguistic Truth Tables 153 9.3 Approximate and Plausible Reasoning 156 9.4 Fuzzy Languages 160 9.5 Support Logic Programming and Fril ...
Page xii
... reasoning . 409 Figure 15-22 Figure 15-23 Membership functions for several linguistic terms . Comparison of work force algorithms . 413 416 Figure 15-24 Flowtime of a course . 421 Figure 15-25 The scheduling process . 422 Figure 15-26 ...
... reasoning . 409 Figure 15-22 Figure 15-23 Membership functions for several linguistic terms . Comparison of work force algorithms . 413 416 Figure 15-24 Flowtime of a course . 421 Figure 15-25 The scheduling process . 422 Figure 15-26 ...
Page xix
... reasoning ( 9 ) , on expert systems and fuzzy control ( 10 ) , on decision making ( 12 ) , and on fuzzy set models in oper- ations research ( 13 ) have been restructured and rewritten . Exercises have been added to almost all chapters ...
... reasoning ( 9 ) , on expert systems and fuzzy control ( 10 ) , on decision making ( 12 ) , and on fuzzy set models in oper- ations research ( 13 ) have been restructured and rewritten . Exercises have been added to almost all chapters ...
Page 1
... reasoning , and computing are 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 ...
... reasoning , and computing are 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 ...
Page 3
... reasoning , learning , and so on . Some reasons for this fuzziness have already been mentioned . Others are that most of our daily communication uses “ natural languages , ” and a good part of our thinking is done in it INTRODUCTION TO ...
... reasoning , learning , and so on . Some reasons for this fuzziness have already been mentioned . Others are that most of our daily communication uses “ natural languages , ” and a good part of our thinking is done in it INTRODUCTION TO ...
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 à 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 µÃ(x µµ(x