A self-organized rule generation scheme for fuzzy controllers

We present a three stage hierarchical self-organized genetic-algorithm based rule generation (SOGARG) method for fuzzy controllers. The first stage selects rules required to control the system in the vicinity of the set point. The second stage extends the rule base to span the entire input space. The third stage then refines the rule-base. The first two stages use the same fitness function, but the last stage uses a different one, which attempts to optimize both the settling time and number of rules retaining the controllability of the system. The mutation operation used in different stages are different to make them consistent with the goal of different stages. The effectiveness of SOGARG has been demonstrated on the inverted pendulum for which we get a rule set containing only about 5% of all possible fuzzy rules