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A survey of defuzzification strategies
Author(s) -
Roychowdhury Shounak,
Pedrycz Witold
Publication year - 2001
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.1030
Subject(s) - defuzzification , fuzzy set operations , fuzzy classification , fuzzy set , fuzzy number , fuzzy logic , type 2 fuzzy sets and systems , fuzzy associative matrix , computer science , data mining , fuzzy control system , artificial intelligence , neuro fuzzy , mathematics
Defuzzification is an important operation in the theory of fuzzy sets. It transforms a fuzzy set information into a numeric data information. This operation along with the operation of fuzzification is critical to the design of fuzzy systems as both of these operations provide nexus between the fuzzy set domain and the real‐valued scalar domain. We need the synergy of both of these domains to solve many of our ill‐posed problems effectively. In this paper, we address the problem of defuzzification, we present merits and demerits of various defuzzification strategies that are used in the theory and practice, and in design and implementation of applications involving fuzzy theory, fuzzy control, and fuzzy rule base, and fuzzy inference‐based systems. We also present in this paper a simple and yet a novel defuzzification mechanism. © 2001 John Wiley & Sons, Inc.