Adapting the Gain of anFLC with

Fuzzy Logic Controllers are knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions. The deenition of these Fuzzy Rules and Fuzzy Membership Functions is generally aaected by subjective decisions, having a great innuence over the performance of the Fuzzy Controller. In some cases, the Membership Functions are deened within a normalized interval, and the Knowledge Base includes a set of scaling functions to convert the input variables from its real value to a normalized one, and the normalized outputs to its real value. Diierent works have proposed the application of genetic strategies, with a learning purpose, to the Knowledge Base of Fuzzy Controllers. The learning is usually centered on the Membership Functions and/or the Rule Base, using xed (predeened) scaling functions. In this paper, the evolution is applied to modify the gain of the controller (by modifying the scaling function of each input or output variable), and the Rule Base. The use of linear and non-linear scaling functions is analyzed.