## 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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**chapters**have been updated. The**chapters**on possibility theory (8), on fuzzy logic and approximate reasoning (9), on expert systems and fuzzy control (10), on decision making (12), and on fuzzy set models in operations research (13) ... Page xxi

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**chapters**have been updated.**Chapters**9, 10, 11, and 12 have been completely rewritten. ... I would like to thank Mr. Tore Grünert for his active participation and contributions, particularly to**chapter**11, and all my coworkers for ... Page xxii

butions, particularly to

butions, particularly to

**chapter**11, and all my coworkers for helping to proofread the book and to prepare new figures. We all hope that this third edition will benefit future students and accelerate the broader acceptance of fuzzy set ... Page xxiv

This situation is mirrored in this edition of the book by an extension of the

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 ... Page xxv

In

In

**chapter**11 primarily a section for defuzzification has been added for the Same reaSOI).**Chapter**12 has been added because the application of fuzzy technology in information processing ...### 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