Nonlinear context adaptation in the calibration of fuzzy sets

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 Script A = {A1, A2,...,An} defined over [0,1], the context adaptation gives rise to a new frame of cognition Script A' = {A'1, A'2,...,A'n} expressed over [a,b]. Owing to the inherent nonlinearity of the developed mapping, different elements (fuzzy sets) of Script A can be "stretched" or "expanded" according to the given experimental data. Proposed is a neural network as a relevant optimization tool. © 1997 Elsevier Science B.V.