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
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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 ix
The maximum of a fuzzy function. Fuzzily bounded interval. Uncertainty as situational property. Probability of a fuzzy event. Linguistic variable “Age". Linguistic variable “Probability”. Linguistic variable “Truth".
The maximum of a fuzzy function. Fuzzily bounded interval. Uncertainty as situational property. Probability of a fuzzy event. Linguistic variable “Age". Linguistic variable “Probability”. Linguistic variable “Truth".
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 xvi
Another important issue addressed in Professor Zimmermann's treatise relates to the distinction between the concepts of probability and possibility, with the latter concept having a close connection with that of membership in a fuzzy ...
Another important issue addressed in Professor Zimmermann's treatise relates to the distinction between the concepts of probability and possibility, with the latter concept having a close connection with that of membership in a fuzzy ...
Page 3
This type of uncertainty (stochastic character) has long been handled appropriately by probability theory and statistics. This Kolmogoroff-type probability is essentially frequentistic and is based on set-theoretic considerations.
This type of uncertainty (stochastic character) has long been handled appropriately by probability theory and statistics. This Kolmogoroff-type probability is essentially frequentistic and is based on set-theoretic considerations.
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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
Bibliographic information
| Title | Fuzzy Set Theory—and Its Applications SpringerLink : Bücher |
| Author | Hans-Jürgen Zimmermann |
| Edition | 4, illustrated |
| Publisher | Springer Science & Business Media, 2011 |
| ISBN | 9401006466, 9789401006460 |
| Length | 514 pages |
| Subjects | › › Business & Economics / Operations Research Computers / Artificial Intelligence / General Mathematics / General Mathematics / History & Philosophy Mathematics / Logic |
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