## 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 xviii

The first volume contains the basic

The first volume contains the basic

**theory**of**fuzzy sets**and some areas of application. It is intended to provide extensive coverage of the**theoretical**and applicational approaches to**fuzzy sets**. Sophisticated formalisms have not been ... Page xix

Since this book was first published in 1985,

Since this book was first published in 1985,

**Fuzzy Set Theory**has had an unexpected growth. It was further developed theoretically and it was applied to new areas. A number of very good books have appeared, primarily dedicated to ... Page xx

[Zimmermann 1987] focuses on decision making and expert systems and introduces

[Zimmermann 1987] focuses on decision making and expert systems and introduces

**fuzzy set theory**only where and to the extent that it is needed, this book tries to offer a didactically prepared text which requires hardly any special ... Page xxiii

These are terms which have been coined in the first half of the 90s, when

These are terms which have been coined in the first half of the 90s, when

**fuzzy set theory**, neural networks and evolutionary computing joined forces because they felt that there were strong synergies between these areas. 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

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 ...### What people are saying - Write a review

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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