Elicitation and fine-tuning of fuzzy control rules using symbiotic evolution

This paper exploits the ability of Symbiotic Evolution (SE), as a generic methodology, to elicit a fuzzy rule-base of the Mamdani-type. Almost all fuzzy rule-base generation algorithms produce rule-bases with redundant and overlapped membership functions that limit their interpretability elegance in their application. We address this problem by applying an algorithm to merge any similar membership functions. It is shown that our proposed algorithm leads generally to a more transparent and more interpretable rule-base with a minimum number of membership functions and a reduced number of rules. In addition, a new post-processing approach is proposed for recovering any probable performance lost after membership functions merging. The proposed methodology has been applied successfully for the design of an active control suspension system using a non-linear Bond Graphs (BG) based half-car model with parameters that relate to a Ford Fiesta MK2.