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 v
... Sets—Basic Definitions Basic Definitions Basic Set-Theoretic Operations for Fuzzy Sets Extensions Types of Fuzzy ... Measures of Fuzziness Fuzzy Measures Measures of Fuzziness The Extension Principle and Applications The Extension ...
... Sets—Basic Definitions Basic Definitions Basic Set-Theoretic Operations for Fuzzy Sets Extensions Types of Fuzzy ... Measures of Fuzziness Fuzzy Measures Measures of Fuzziness The Extension Principle and Applications The Extension ...
Page vii
... Tools Stability Extensions Fuzzy Data Bases and Queries Introduction Fuzzy Relational Databases Fuzzy Queries in Crisp Databases Fuzzy Data Analysis Introduction Methods for Fuzzy Data Analysis Algorithmic Approaches Knowledge-Based ...
... Tools Stability Extensions Fuzzy Data Bases and Queries Introduction Fuzzy Relational Databases Fuzzy Queries in Crisp Databases Fuzzy Data Analysis Introduction Methods for Fuzzy Data Analysis Algorithmic Approaches Knowledge-Based ...
Page ix
The extension principle. Trapezoidal “fuzzy number”. LR-representation of fuzzy numbers. Fuzzy graphs. Fuzzy forests. Graphs that are not forests. Maximizing set. A fuzzy function. Triangular fuzzy numbers representing a fuzzy function.
The extension principle. Trapezoidal “fuzzy number”. LR-representation of fuzzy numbers. Fuzzy graphs. Fuzzy forests. Graphs that are not forests. Maximizing set. A fuzzy function. Triangular fuzzy numbers representing a fuzzy function.
Page xviii
I have tried to present the basic theory and its extensions in enough detail to be comprehended by those who have not been exposed to fuzzy set theory. Examples and exercises serve to illustrate the concepts even more clearly.
I have tried to present the basic theory and its extensions in enough detail to be comprehended by those who have not been exposed to fuzzy set theory. Examples and exercises serve to illustrate the concepts even more clearly.
Page xxiv
This situation is mirrored in this edition of the book by an extension of the chapter on data mining and a new chapter on fuzzy sets in data bases. The following figure indicates the development of fuzzy set theory from another point of ...
This situation is mirrored in this edition of the book by an extension of the chapter on data mining and a new chapter on fuzzy sets in data bases. The following figure indicates the development of fuzzy set theory from another point of ...
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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 |
| Export Citation | BiBTeX EndNote RefMan |