## 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

Page v

List of

List of

**Figures**List of Tables ForeWord Preface Preface to the Fourth Edition Introduction to**Fuzzy**Sets Crispness, Vagueness, Fuzziness, Uncertainty**Fuzzy**Set Theory**Fuzzy**Mathematics**Fuzzy**Sets—Basic Definitions Basic Definitions ... Page ix

Real

Real

**numbers**close to 10. Convex**fuzzy**set. Nonconvex**fuzzy**set. Union and intersection of**fuzzy**sets.**Fuzzy**sets vs. probabilistic sets. Mapping of t-norms, t-conorms, and averaging operators. The extension principle. Page 13

Real

Real

**numbers**close to 10. 3. ... close to 10” A = 0.1/7+0.5/8+0.8/94.1/10+0.8/11+0.5/12+0.1/13 Example 2–1e A = “real**numbers**close to 10” - 1 A = | — ... A nonempty**fuzzy**set A can always be normalized by dividing pi(x) by suppli(x). Page 20

Model the following expressions as

Model the following expressions as

**fuzzy**sets: . Large integers . Very small**numbers**. Medium-sized men .**Numbers**approximately between 10 and 20 : e. High speeds for racing cars 2. Determine all O-level sets and all strong O-level sets ... Page 22

3.1 Types of

3.1 Types of

**Fuzzy**Sets In chapter 2, the basic definition of a**fuzzy**set was given and the original settheoretic operations were discussed. The membership space was assumed to be the space of real**numbers**, membership functions were ...### What people are saying - Write a review

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### Contents

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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