Readings in Fuzzy Sets for Intelligent Systems

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Synopsis

Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.

Book details

Author:
Didier J. Dubois, Henri Prade, Ronald R. Yager
ISBN:
9781483214504
Related ISBNs:
9781558602571, 9781558602571
Publisher:
Morgan Kaufmann Publishers
Pages:
928
Reading age:
Not specified
Includes images:
No
Date of addition:
2021-03-26
Usage restrictions:
Copyright
Copyright date:
1993
Copyright by:
Elsevier Science & Technology 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Mathematics and Statistics, Nonfiction