Context adaptation in fuzzy processing and genetic algorithms

In this paper we introduce the use of contextual transformation functions to adjust membership functions in fuzzy systems. We address both linear and nonlinear functions to perform linear or nonlinear context adaptation, respectively. The key issue is to encode knowledge in a standard frame of reference, and have its meaning tuned to the situation by means of an adequate transformation reflecting the influence of context in the interpretation of a concept. Linear context adaptation is simple and fast. Nonlinear context adaptation is more computationally expensive, but due to its nonlinear characteristic, different parts of base membership functions can be stretched or expanded to best fit the desired format. Here we use a genetic algorithm to find a nonlinear transformation function, given the base membership functions and a set of data extracted from environment classified by means of fuzzy concepts.