A design of CMAC-based FLC with fast learning and accurate approximation

Proposes a CMAC-based FLC (fuzzy logic controller) with a fast learning capability and an accurate approximation ability. The proposed CMAC-based FLC has the fast learning capability because it pursues the local generalization and only a small number of activated units in the network participate in the forward and backward computation. It also produces an accurate input-output approximation ability because it adjusts the membership function's model parameters of the input and output variables simultaneously and it considers both centers and widths of output membership functions to compute a crisp defuzzified value. Application to the truck backer-upper control problem of the proposed CMAC-based FLC is presented. Simulation results validate the fast learning and the accurate approximation of the proposed CMAC-based FLC.