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 86
Page vii
... Fuzzy Data Analysis Introduction Methods for Fuzzy Data Analysis Algorithmic Approaches Knowledge-Based Approaches Neural Net Approaches Dynamic Fuzzy Data Analysis Problem Description Similarity of Functions Approaches for Analysic ...
... Fuzzy Data Analysis Introduction Methods for Fuzzy Data Analysis Algorithmic Approaches Knowledge-Based Approaches Neural Net Approaches Dynamic Fuzzy Data Analysis Problem Description Similarity of Functions Approaches for Analysic ...
Page viii
... Engineering Applications Linguistic Evaluation and Ranking of Machine Tools Fault Detection in Gearboxes Applications in Management A Discrete Location Model Fuzzy Set Models in Logistics Fuzzy Approach to the Transportation Problem ...
... Engineering Applications Linguistic Evaluation and Ranking of Machine Tools Fault Detection in Gearboxes Applications in Management A Discrete Location Model Fuzzy Set Models in Logistics Fuzzy Approach to the Transportation Problem ...
Page xviii
It is intended to provide extensive coverage of the theoretical and applicational approaches to fuzzy sets. Sophisticated formalisms have not been included. I have tried to present the basic theory and its extensions in enough detail to ...
It is intended to provide extensive coverage of the theoretical and applicational approaches to fuzzy sets. Sophisticated formalisms have not been included. I have tried to present the basic theory and its extensions in enough detail to ...
Page 3
In both types of probabilistic approaches, however, it is assumed that the events (elements of sets) or the statements, respectively, are well defined. We shall call this type of uncertainty or vagueness stochastic uncertainty in ...
In both types of probabilistic approaches, however, it is assumed that the events (elements of sets) or the statements, respectively, are well defined. We shall call this type of uncertainty or vagueness stochastic uncertainty in ...
Page 8
Comparing the results achieved by these two alternative approaches showed that the accuracy of the results was almost the same for all practical purposes. This is particularly true if one considers the inaccuracies and uncertainties ...
Comparing the results achieved by these two alternative approaches showed that the accuracy of the results was almost the same for all practical purposes. This is particularly true if one considers the inaccuracies and uncertainties ...
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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