Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Theoretical aspects of fuzzy control
Theoretical aspects of fuzzy control
Optimal strategy of switching reasoning methods in fuzzy control
Theoretical aspects of fuzzy control
Fuzzy Systems: Modeling and Control
Fuzzy Systems: Modeling and Control
A First Course in Fuzzy and Neural Control
A First Course in Fuzzy and Neural Control
A First Course in Fuzzy Logic, Third Edition
A First Course in Fuzzy Logic, Third Edition
Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic
Information Sciences: an International Journal
Editorial: Special Issue on Hybrid Intelligent Systems
Information Sciences: an International Journal
An image coding/decoding method based on direct and inverse fuzzy transforms
International Journal of Approximate Reasoning
Editorial to the special issue on high order fuzzy sets
Information Sciences: an International Journal
Fuzzy transforms: Theory and applications
Fuzzy Sets and Systems
Type-2 fuzzy logic controllers: a way forward for fuzzy systems in real world environments
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
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In many practical applications, it turns out to be useful to use the notion of fuzzy transform: once we have functions A1(x) ≥ 0, ...,An ≥ 0, with Σi=1n Ai(x) = 1, we can then represent each function f (x) by the coefficients Fi = (∫ f (x) ċAi(x)dx)/(∫ Ai(x)dx). Once we know the coefficients Fi, we can (approximately) reconstruct the original function f(x) as Σi=1n Fi ċ Ai(x). The original motivation for this transformation came from fuzzy modeling, but the transformation itself is a purely mathematical transformation. Thus, the empirical successes of this transformation suggest that this transformation can be also interpreted in more traditional (nonfuzzy) mathematics as well. Such an interpretation is presented in this paper. Specifically, we show that the 2002 probabilistic interpretation of fuzzy modeling by Sánchez et al. can be modified into a natural probabilistic explanation of fuzzy transform formulas.