## 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

Results 1-5 of 31

Page xx

[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

[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 3

The complete description of a real system often would

The complete description of a real system often would

**require**far more detailed data than a human being could ever recognize simultaneously, process, and understand. This situation has already been recognized by thinkers in the past. Page 4

... is a multi-facetted phenomenon and that the modeling of it in applicationoriented models

... is a multi-facetted phenomenon and that the modeling of it in applicationoriented models

**requires**considerable investigations before we start the modeling Creditworthiness Financial - Basis Personality Security Liquidity Potential . Page 8

... but

... but

**require**a very solid mathematical background and those that are not of obvious relevance to applications will not be discussed. 2. Most of the discussion will proceed along the lines of the early concepts of fuzzy set theory. Page 19

... of the statement “S and T'

... of the statement “S and T'

**requires**more, and accepting the truth of the statement “S or T'' less than accepting S or Talone as true. v. f(1, 1) = 1 and g(0, 0) = 0. vi. Logically equivalent statements must have equal truth values, ...### What people are saying - Write a review

We haven't found any reviews in the usual places.

### 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 |

### Other editions - View all

### 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