## 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 89

Page vi

... Fuzzy Differentiation Uncertainty Modeling Application-oriented Modeling of Uncertainty Causes of Uncertainty Type of Available Information Uncertainty

... Fuzzy Differentiation Uncertainty Modeling Application-oriented Modeling of Uncertainty Causes of Uncertainty Type of Available Information Uncertainty

**Methods**Uncertainty Theories as Transformers of Information Matching Uncertainty ... Page vii

... Fuzzy Data Analysis Introduction

... Fuzzy Data Analysis Introduction

**Methods**for Fuzzy Data Analysis Algorithmic Approaches Knowledge-Based Approaches Neural Net Approaches Dynamic Fuzzy Data Analysis Problem Description Similarity of Functions Approaches for Analysic ... Page viii

... Sets in Scheduling Job-Shop Scheduling with Expert Systems A

... Sets in Scheduling Job-Shop Scheduling with Expert Systems A

**Method**to Control Flexible Manufacturing Systems Aggregate Production and Inventory Planning Fuzzy Mathematical Programming for Maintenance Scheduling Scheduling Courses, ... Page xv

In the two decades since its inception, the theory has matured into a wideranging collection of concepts and techniques for dealing with complex phenomena that do not lend themselves to analysis by classical

In the two decades since its inception, the theory has matured into a wideranging collection of concepts and techniques for dealing with complex phenomena that do not lend themselves to analysis by classical

**methods**based on probability ... Page xxiii

Particularly between fuzzy set theory and neural nets the synergies have been used to develop hybrid models and

Particularly between fuzzy set theory and neural nets the synergies have been used to develop hybrid models and

**methods**, that combine the strengths of both of these areas. Nevertheless, all three areas are continuing to develop new ...### What people are saying - Write a review

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### Contents

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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