论文部分内容阅读
充分利用了TSK(Sugeno Tanaka)模糊系统的优点 ,提出了改进的CMAC(CerebellarModelAr ticulationController)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 advantages of TSK (Sugeno Tanaka) fuzzy system are fully utilized. An improved super closed ball network (CMAC) network is proposed and its learning algorithm is given. The experimental results show that the improved CMAC network has more advantages than CMAC The closed-ball structure network has higher approximation accuracy to the sample.