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. |
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Page xv
... given rise to an extensive literature dealing with t - norms and related concepts that link some aspects of the theory of fuzzy sets to the theory of probabilistic metric spaces developed by Karl Menger . Another important issue ...
... given rise to an extensive literature dealing with t - norms and related concepts that link some aspects of the theory of fuzzy sets to the theory of probabilistic metric spaces developed by Karl Menger . Another important issue ...
Page 11
... of membership for the elements of a given set . Definition 2-1 If X is a collection of objects denoted generically by x , then a fuzzy set à in X is a set of ordered pairs : à = { ( x , μ2 ( x ) Fuzzy Sets-Basic Definitions.
... of membership for the elements of a given set . Definition 2-1 If X is a collection of objects denoted generically by x , then a fuzzy set à in X is a set of ordered pairs : à = { ( x , μ2 ( x ) Fuzzy Sets-Basic Definitions.
Page 25
... given in definition 2-1 is that of an L - fuzzy set [ Goguen 1967 ; De Luca and Termini 1972 ] . In contrast to the above definition , the membership function of an L - fuzzy set maps into a partially ordered set , L. Since the interval ...
... given in definition 2-1 is that of an L - fuzzy set [ Goguen 1967 ; De Luca and Termini 1972 ] . In contrast to the above definition , the membership function of an L - fuzzy set maps into a partially ordered set , L. Since the interval ...
Page 27
... given by the equivalence relation R and the approximation space may not , in general , be replaced by a membership function similar to that introduced by Zadeh . In order to take probabilistic informations crucial to nondeterministic ...
... given by the equivalence relation R and the approximation space may not , in general , be replaced by a membership function similar to that introduced by Zadeh . In order to take probabilistic informations crucial to nondeterministic ...
Page 35
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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