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 1-5 of 31
Page xx
... requires hardly any special math- ematical background of the reader . It tries to introduce fuzzy set theory as com- prehensively as possible , without delving into very theoretical areas or presenting any mathematical proofs which do ...
... requires hardly any special math- ematical background of the reader . It tries to introduce fuzzy set theory as com- prehensively as possible , without delving into very theoretical areas or presenting any mathematical proofs which do ...
Page 3
... require far more detailed data than a 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 ...
... require far more detailed data than a 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 ...
Page 4
... in application- oriented models requires considerable investigations before we start the modeling Financial Basis Credit- worthiness Personality Security Liquidity Potential Business Behavior 4 FUZZY SET THEORY - AND ITS APPLICATIONS.
... in application- oriented models requires considerable investigations before we start the modeling Financial Basis Credit- worthiness Personality Security Liquidity Potential Business Behavior 4 FUZZY SET THEORY - AND ITS APPLICATIONS.
Page 8
... require a very solid math- ematical background and those that are not of obvious relevance to applica- tions will not be discussed . 2. Most of the discussion will proceed along the lines of the early concepts of fuzzy set theory . At ...
... require a very solid math- ematical background and those that are not of obvious relevance to applica- tions will not be discussed . 2. Most of the discussion will proceed along the lines of the early concepts of fuzzy set theory . At ...
Page 19
... requires more , and accepting the truth of the statement " S or T ” less than accepting S or T alone as true . v . f ( 1 , 1 ) = 1 and g ( 0 , 0 ) = 0 . vi . Logically equivalent statements must have equal truth values , and fuzzy sets ...
... requires more , and accepting the truth of the statement " S or T ” less than accepting S or T alone as true . v . f ( 1 , 1 ) = 1 and g ( 0 , 0 ) = 0 . vi . Logically equivalent statements must have equal truth values , and fuzzy sets ...
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