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 xviii
... human decision making . It is self - contained in the sense that all concepts used are properly introduced and defined . Obviously this cannot be done in the same breadth as in the first volume . Also the coverage of fuzzy concepts in ...
... human decision making . It is self - contained in the sense that all concepts used are properly introduced and defined . Obviously this cannot be done in the same breadth as in the first volume . Also the coverage of fuzzy concepts in ...
Page xxiii
... humans with computers and modeling certain uncertainties . Particularly between fuzzy set theory and neural nets the synergies have been used to develop hybrid models and methods , that combine the strengths of both of these areas ...
... humans with computers and modeling certain uncertainties . Particularly between fuzzy set theory and neural nets the synergies have been used to develop hybrid models and methods , that combine the strengths of both of these areas ...
Page 2
... Human thinking and feeling , in which ideas , pictures , images , and value systems are formed , first of all certainly has more concepts or comprehensions than our daily language has words . If one considers , in addition , that for a ...
... Human thinking and feeling , in which ideas , pictures , images , and value systems are formed , first of all certainly has more concepts or comprehensions than our daily language has words . If one considers , in addition , that for a ...
Page 3
... human being could ever recognize simultaneously , process , and understand . This situation has already been recognized by thinkers in the past . In 1923 the philosopher B. Russell [ 1923 ] referred to the first point when he wrote ...
... human being could ever recognize simultaneously , process , and understand . This situation has already been recognized by thinkers in the past . In 1923 the philosopher B. Russell [ 1923 ] referred to the first point when he wrote ...
Page 4
... human being could handle simultaneously . Therefore the term , which in psychology is called a “ subjective category , " becomes fuzzy . One could imagine that the subjective category “ creditworthiness ” is decomposed into two smaller ...
... human being could handle simultaneously . Therefore the term , which in psychology is called a “ subjective category , " becomes fuzzy . One could imagine that the subjective category “ creditworthiness ” is decomposed into two smaller ...
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