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 xii
... respect to the center of class 2 ) in time window ( 250 , 350 ) . Membership functions for time window ( 3014 , 3114 ) . Membership functions for time window ( 3064 , 3200 ) . Road network . Figure 15-6 Figure 15-7 Figure 15-8 Figure 15 ...
... respect to the center of class 2 ) in time window ( 250 , 350 ) . Membership functions for time window ( 3014 , 3114 ) . Membership functions for time window ( 3064 , 3200 ) . Road network . Figure 15-6 Figure 15-7 Figure 15-8 Figure 15 ...
Page xxv
... respect to t - norms , other operators and uncertainty modeling because I am convinced that chapters 2 to 7 are still sufficient as a mathematical basis to under- stand all new developments in this area and also for part II of the book ...
... respect to t - norms , other operators and uncertainty modeling because I am convinced that chapters 2 to 7 are still sufficient as a mathematical basis to under- stand all new developments in this area and also for part II of the book ...
Page 4
... respect to their origins : intrinsic fuzziness and informational fuzziness . The former is the fuzziness to which Russell's remark referred , and it is illustrated by " tall men . " This term is fuzzy because the meaning of tall is ...
... respect to their origins : intrinsic fuzziness and informational fuzziness . The former is the fuzziness to which Russell's remark referred , and it is illustrated by " tall men . " This term is fuzzy because the meaning of tall is ...
Page 5
... respects the framework used in the case of ordinary sets , but is more general than the latter and , potentially , may prove to have a much wider scope of applicability , particularly in the fields of pattern classification and ...
... respects the framework used in the case of ordinary sets , but is more general than the latter and , potentially , may prove to have a much wider scope of applicability , particularly in the fields of pattern classification and ...
Page 19
... ( see ( vi ) ) with respect to one another . 1. με 1 μ . = με με Λ Λ με ν με = με να με 2. ( μs ^ μT ) ^ μv = μs ^ ( μT ^ μU ) ( με ν μπ ) ν μυ = με ν ( με ν μυ ) 3. μs ^ ( μT V μv ) = ( FUZZY SETS - BASIC DEFINITIONS 19.
... ( see ( vi ) ) with respect to one another . 1. με 1 μ . = με με Λ Λ με ν με = με να με 2. ( μs ^ μT ) ^ μv = μs ^ ( μT ^ μU ) ( με ν μπ ) ν μυ = με ν ( με ν μυ ) 3. μs ^ ( μT V μv ) = ( FUZZY SETS - BASIC DEFINITIONS 19.
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