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
Reliable Computing – Springer Journals
Published: Oct 3, 2004
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