## 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 xii

**Concept**hierarchy of creditworthiness together with individual weights d and g-values for each level of aggregation. 384 464 468 469 472 473 474 475 Table 3–1 Table 3–2 Table 3–3 Table 6–1 Table 8–1 xii LIST OF FIGURES. Page xv

As its name implies, the theory of fuzzy sets is, basically, a theory of graded

As its name implies, the theory of fuzzy sets is, basically, a theory of graded

**concepts**—a theory in which everything is a matter of degree or, to put it figuratively, everything has elasticity. In the two decades since its inception, ... Page xvi

Another important issue addressed in Professor Zimmermann's treatise relates to the distinction between the

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 xviii

Examples and exercises serve to illustrate the

Examples and exercises serve to illustrate the

**concepts**even more clearly. For the interested or more advanced reader, numerous references to recent literature are included that should facilitate studies of specific areas in more detail ... Page xxiv

This was valid until the first “fuzzy booms” occurred in the first half of the 90s. Until then the development of applications and technology centered very much around fuzzy control, a

This was valid until the first “fuzzy booms” occurred in the first half of the 90s. Until then the development of applications and technology centered very much around fuzzy control, a

**concept**that was very applicable, easy.### 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