基于TSK模糊系统的改进CMAC超闭球网络结构

摘要

充分利用了TSK(Sugeno-Tanaka)模糊系统的优点，提出了改进的CMAC(Cerebellar Model Articulation Controller)超闭球结构网络，给出了其学习算法，实验结果表明改进的CMAC超闭球结构网络较之CMAC超闭球结构网络对样本有更高的逼近精度。%The advantage of TSK fussy system(Sugeno-Tanaka fuzzy system) is combined into CMAC(Cerebellar Model Articulation Controller) neural network, and therefore the improved CMAC neural network is presented and its learning algorithm. Our experiment results have showed that the Improved-CMAC outperforms CMAC in learning precision.