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 adaptation gives rise to a new frame of cognition
A
′= {
A
1′,
A
2′, …,
A
n
′} expressed over [
a,
b]. Owing 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.