Context adaptation of Mamdani fuzzy systems through new operators tuned by a genetic algorithm

Context adaptation can be achieved by adjusting an initial normalized fuzzy rule-based system through the use of operators that appropriately change the representation of the linguistic variables. The choice of the specific operators and their parameters should be context-based and optimized so as to obtain a good interpretability accuracy tradeoff. In this paper, we propose a set of context adaptation operators that, starting from a given fuzzy systems, adjust some of its components, such as fuzzy set support and core, membership function shape , etc. We use a genetic timing process for choosing the operator parameters. We finally describe the application of the proposed operators to Mamdam fuzzy systems with reference to two real examples.