A Special Session on Granular Computing and Interval Computations at the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS) Atlanta, Georgia, July 13–15, 2000

A Special Session on Granular Computing and Interval Computations at the 19th International... A special session on Granular Computing and Interval Computations at the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS) Atlanta, Georgia, July 13–15, 2000 In many application areas, we do not have an exact model of the situation and of the objects and processes that we want to analyze and to control. Instead, we have expert knowledge about these objects and processes, knowledge which experts can often only describe by using imprecise (“fuzzy”) words and terms from natural languages such as “small,” “significant,” etc. To enable computers to use this knowledge, it is necessary to reformulate it in computer-understandable terms, and then be able to process thus reformulated knowledge. Techniques for reformulating and processing such “linguistic” (natural-language) knowledge were proposed by Lotfi Zadeh in early 1960’s under the name of “fuzzy techniques.” In the past decades, these techniques have been successfully used in many application areas, from control to expert systems to medicine. The basic mathematical object of fuzzy techniques—a fuzzy set—can be viewed as a nested family of sets (in 1-D case, the nested family of intervals) each of which represent the set of values which are possible with a given degree of certainty http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

A Special Session on Granular Computing and Interval Computations at the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS) Atlanta, Georgia, July 13–15, 2000

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2001 by Kluwer Academic Publishers
Subject
Mathematics; Numeric Computing; Approximations and Expansions; Computational Mathematics and Numerical Analysis; Mathematical Modeling and Industrial Mathematics
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1023/A:1011468210109
Publisher site
See Article on Publisher Site

Abstract

A special session on Granular Computing and Interval Computations at the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS) Atlanta, Georgia, July 13–15, 2000 In many application areas, we do not have an exact model of the situation and of the objects and processes that we want to analyze and to control. Instead, we have expert knowledge about these objects and processes, knowledge which experts can often only describe by using imprecise (“fuzzy”) words and terms from natural languages such as “small,” “significant,” etc. To enable computers to use this knowledge, it is necessary to reformulate it in computer-understandable terms, and then be able to process thus reformulated knowledge. Techniques for reformulating and processing such “linguistic” (natural-language) knowledge were proposed by Lotfi Zadeh in early 1960’s under the name of “fuzzy techniques.” In the past decades, these techniques have been successfully used in many application areas, from control to expert systems to medicine. The basic mathematical object of fuzzy techniques—a fuzzy set—can be viewed as a nested family of sets (in 1-D case, the nested family of intervals) each of which represent the set of values which are possible with a given degree of certainty

Journal

Reliable ComputingSpringer Journals

Published: Oct 3, 2004

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