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 46
Page 6
... classical mathematical areas such as algebra , graph theory , topology , and so on by generalizing ( fuzzifying ) them . 2. As an application - oriented " fuzzy technology " , i.e. as a tool for modeling , problem solving and data ...
... classical mathematical areas such as algebra , graph theory , topology , and so on by generalizing ( fuzzifying ) them . 2. As an application - oriented " fuzzy technology " , i.e. as a tool for modeling , problem solving and data ...
Page 7
... Classical models and methods are normally based on dual logic . They , therefore , distinguish between feasible and infeasible , belonging to a cluster or not , optimal or suboptimal etc. Often this view does not capture reality ...
... Classical models and methods are normally based on dual logic . They , therefore , distinguish between feasible and infeasible , belonging to a cluster or not , optimal or suboptimal etc. Often this view does not capture reality ...
Page 9
... Chapter 8 focuses on uncertainty modeling and some special topics , such as the relationship between fuzzy set theory , probability theory , and other classical areas . 2 FUZZY SETS BASIC DEFINITIONS 2.1 Basic Definitions A classical.
... Chapter 8 focuses on uncertainty modeling and some special topics , such as the relationship between fuzzy set theory , probability theory , and other classical areas . 2 FUZZY SETS BASIC DEFINITIONS 2.1 Basic Definitions A classical.
Page 11
... classical set can be described in different ways : one can either enu- merate ( list ) the elements that belong to the set ; describe the set analytically , for instance , by stating conditions for membership ( A = { xlx ≤ 5 } ) ; or ...
... classical set can be described in different ways : one can either enu- merate ( list ) the elements that belong to the set ; describe the set analytically , for instance , by stating conditions for membership ( A = { xlx ≤ 5 } ) ; or ...
Page 13
... classical set and the member- ship function a generalization of the characteristic function . Since we are gener- ally referring to a universal ( crisp ) set X , some elements of a fuzzy set may have the degree of membership zero ...
... classical set and the member- ship function a generalization of the characteristic function . Since we are gener- ally referring to a universal ( crisp ) set X , some elements of a fuzzy set may have the degree of membership zero ...
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