Fuzzy Set Theory—and Its Applications
Springer Science & Business Media, Jun 27, 2011 - Mathematics - 514 pages
Since 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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Basic SetTheoretic Operations for Fuzzy Sets
Criteria for Selecting Appropriate Aggregation Operators
The Extension Principle and Applications
Special Extended Operations
Applicationoriented Modeling of Uncertainty
Fuzzy Data Bases and Queries
Decision Making in Fuzzy Environments
Applications of Fuzzy Sets in Engineering and Management
Empirical Research in Fuzzy Set Theory
Fuzzy Relations and Fuzzy Graphs
Fuzzy Functions on Fuzzy Sets
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