Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/62009
Type: Artigo de periódico
Title: Nonlinear context adaptation in the calibration of fuzzy sets
Author: Pedrycz, W
Gudwin, RR
Gomide, FAC
Abstract: In this note we elaborate on the concept and use of context adaptation. The underlying idea hinges upon a nonlinear transformation of an actual reference unit universe of discourse into a subset of reals, say [a, b], that is implied by actually available data (current context). Assuming a collection of fuzzy sets A = {A(1), A(2),...,A(n)} defined over [0, 1], the context adaptation gives rise to a new frame of cognition A' = (A'(1), A'(2),...,A'(g)) expressed over [a,b]. Owing to the inherent nonlinearity of the developed mapping, different elements (fuzzy sets) of A can be ''stretched'' or ''expanded'' according to the given experimental data. Proposed is a neural network as a relevant optimization tool. (C) 1997 Elsevier Science B.V.
Subject: context adaptation
frame of cognition
knowledge representation
information granularity
neurocomputing
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/S0165-0114(96)00057-7
Date Issue: 1997
Appears in Collections:Unicamp - Artigos e Outros Documentos

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