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 viii
... Multicriteria Analysis Multi Objective Decision Making (MODM) Multi Attributive Decision Making (MADM) Applications of Fuzzy Sets in Engineering and Management Introduction Engineering Applications Linguistic Evaluation and Ranking ...
... Multicriteria Analysis Multi Objective Decision Making (MODM) Multi Attributive Decision Making (MADM) Applications of Fuzzy Sets in Engineering and Management Introduction Engineering Applications Linguistic Evaluation and Ranking ...
Page xiii
Selected applications in management and engineering. Experimental Data. Surface quality parameters (output data). Boundary values of the linguistic variable “significance". Table 15–5a Populations. 39 40 41 89 121 128 136 137 158 192 ...
Selected applications in management and engineering. Experimental Data. Surface quality parameters (output data). Boundary values of the linguistic variable “significance". Table 15–5a Populations. 39 40 41 89 121 128 136 137 158 192 ...
Page xvii
Applications of this theory can be found, for example, in artificial intelligence, computer science, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, and robotics ...
Applications of this theory can be found, for example, in artificial intelligence, computer science, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, and robotics ...
Page xviii
The target groups were students in business administration, management science, operations research, engineering, and computer science. Even though no specific mathematical background is necessary to understand the books, it is assumed ...
The target groups were students in business administration, management science, operations research, engineering, and computer science. Even though no specific mathematical background is necessary to understand the books, it is assumed ...
Page xxv
This was necessary because the focus of applications here changed, for reasons explained in this chapter, strongly from “engineering intelligence” to “business intelligence”. Of course, the index and the references have also been ...
This was necessary because the focus of applications here changed, for reasons explained in this chapter, strongly from “engineering intelligence” to “business intelligence”. Of course, the index and the references have also been ...
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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