A Fuzzy CMAC Neural Network
一种模糊CMAC神经网络

In this paper a fuzzy CMAC neural network is proposed, which is composed of input layer. fuzzified layer, fuzzy association layer, and output laver. It has the similiar single layer link weights to GMAC and updates the consequence parameters of Takagi's fuzzy reasoning through BP algorithms. foe proposed fuzzy-neural structure is described and the supervised learning algorithm is derived. The simulation results with a function approximation problem problem are shown that the proposed scheme is superior to CMAC in many aspects.