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 vi
Probability Applications of Fuzzy Set Theory Fuzzy Logic and Approximate Reasoning Linguistic Variables Fuzzy Logic Classical Logics Revisited Linguistic Truth Tables Approximate and Plausible Reasoning Fuzzy Languages Support Logic ...
Probability Applications of Fuzzy Set Theory Fuzzy Logic and Approximate Reasoning Linguistic Variables Fuzzy Logic Classical Logics Revisited Linguistic Truth Tables Approximate and Plausible Reasoning Fuzzy Languages Support Logic ...
Page xi
Cracking furnace. Furnace temperature. Fuzzy classification of continuous process. Application of DataFngine for acoustic quality control. A classical decision under certainty. A fuzzy decision. Optimal dividend as maximizing decision.
Cracking furnace. Furnace temperature. Fuzzy classification of continuous process. Application of DataFngine for acoustic quality control. A classical decision under certainty. A fuzzy decision. Optimal dividend as maximizing decision.
Page xv
In the two decades since its inception, the theory has matured into a wideranging collection of concepts and techniques for dealing with complex phenomena that do not lend themselves to analysis by classical methods based on probability ...
In the two decades since its inception, the theory has matured into a wideranging collection of concepts and techniques for dealing with complex phenomena that do not lend themselves to analysis by classical methods based on probability ...
Page 2
However, there are limits to the usefulness and the possibility of using classical mathematical language, based on the dichotomous character of set theory, to model particular systems and phenomena in the social sciences: “There is no ...
However, there are limits to the usefulness and the possibility of using classical mathematical language, based on the dichotomous character of set theory, to model particular systems and phenomena in the social sciences: “There is no ...
Page 5
1.2 Fuzzy Set Theory The first publications in fuzzy set theory by Zadeh [1965) and Goguen [1967, 1969] show the intention of the authors to generalize the classical notion of a set and a proposition [statement] to accommodate fuzziness ...
1.2 Fuzzy Set Theory The first publications in fuzzy set theory by Zadeh [1965) and Goguen [1967, 1969] show the intention of the authors to generalize the classical notion of a set and a proposition [statement] to accommodate fuzziness ...
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Contents
9 | |
11 | |
16 | |
22 | |
29 | |
Criteria for Selecting Appropriate Aggregation Operators | 43 |
The Extension Principle and Applications | 54 |
Special Extended Operations | 61 |
Applicationoriented Modeling of Uncertainty | 111 |
Linguistic Variables | 140 |
Fuzzy Data Bases and Queries | 265 |
Decision Making in Fuzzy Environments | 329 |
Applications of Fuzzy Sets in Engineering and Management | 371 |
Empirical Research in Fuzzy Set Theory | 443 |
Future Perspectives | 477 |
181 | 485 |
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Common terms and phrases
aggregation algorithm analysis applications approach appropriate approximately areas assignment assume base called chapter classical clustering compute concepts considered constraints contains corresponding crisp criteria customers decision defined definition degree of membership depends described determine discussed distribution domain elements engineering example exist expert systems expressed extension Figure fuzzy control fuzzy numbers fuzzy set theory given goal human important indicate inference input instance integral interpreted intersection interval knowledge linguistic variable logic mathematical mean measure membership function methods normally objective objective function observed obtain operators optimal positive possible probability problem programming properties provides reasoning relation representing require respect rules scale shown shows similarity situation solution space specific statement structure suggested t-norms Table tion true truth uncertainty values Zadeh