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
Results 6-10 of 63
Page 16
... called the relative cardinality of A. Obviously , the relative cardinality of a fuzzy set depends on the cardinality of the universe . So you have to choose the same universe if you want to compare fuzzy sets by their relative ...
... called the relative cardinality of A. Obviously , the relative cardinality of a fuzzy set depends on the cardinality of the universe . So you have to choose the same universe if you want to compare fuzzy sets by their relative ...
Page 25
... called the parameter space . One of the main advantages of the notion of probabilistic sets in modeling fuzzy and stochastic features of a system is asserted to be the applicability of moment analysis , that is , the possibility of ...
... called the parameter space . One of the main advantages of the notion of probabilistic sets in modeling fuzzy and stochastic features of a system is asserted to be the applicability of moment analysis , that is , the possibility of ...
Page 27
... called universe and let RC U × U be an equiva- lence relation on U. The pair A = ( U , R ) is called an approximation space . For u , v Є U and ( u , v ) ← R , u and v belong to the same equivalence class , and we say that they are ...
... called universe and let RC U × U be an equiva- lence relation on U. The pair A = ( U , R ) is called an approximation space . For u , v Є U and ( u , v ) ← R , u and v belong to the same equivalence class , and we say that they are ...
Page 30
... the union of fuzzy sets called triangular conorms or t- conorms ( sometimes referred to as s - norms ) is defined analogously [ Dubois and Prade 1985 , p . 90 ; Mizumoto 1989 , 30 FUZZY SET THEORY - AND ITS APPLICATIONS.
... the union of fuzzy sets called triangular conorms or t- conorms ( sometimes referred to as s - norms ) is defined analogously [ Dubois and Prade 1985 , p . 90 ; Mizumoto 1989 , 30 FUZZY SET THEORY - AND ITS APPLICATIONS.
Page 36
You have reached your viewing limit for this book.
You have reached your viewing limit for this book.
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 |
Other editions - View all
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