## 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 x

Terms of “

Terms of “

**degree**of achievement". Aggregation of linguistic variables. Portfolio with linguistic input. ... Neighboring**membership**functions. Separate**membership**functions. Parameters describing fuzzy sets. Influence of symmetry. Page 7

Fuzzy technology has been used to reduce the complexity of data to an acceptably

Fuzzy technology has been used to reduce the complexity of data to an acceptably

**degree**usually either via ... rather than symbols, and arrive at**membership**functions of fuzzy sets, which can then be retranslated into words and ... Page 12

A ={(x, us(x)|xe X} pi(x) is called the

A ={(x, us(x)|xe X} pi(x) is called the

**membership**function or grade of**membership**(also**degree**of compatibility or**degree**of truth) of x in A that maps X to the**membership**space M (When M contains only the two points 0 and 1, ... Page 13

It has already been mentioned that the membership function is not limited to values between 0 and 1. ... Often it is appropriate to consider those elements of the universe that have a nonzero

It has already been mentioned that the membership function is not limited to values between 0 and 1. ... Often it is appropriate to consider those elements of the universe that have a nonzero

**degree of membership**in a fuzzy set. Page 14

Definition 2–3 The (crisp) set of elements that belong to the fuzzy set A at least to the

Definition 2–3 The (crisp) set of elements that belong to the fuzzy set A at least to the

**degree**o is called the O-level ... By contrast to classical set theory, however, convexity conditions are defined with reference to the**membership**...### 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