一种新型的结构自组织基函数模糊CMAC

为提高CMAC的非线性逼近能力 ,通过引入Gauss基函数和基于相似测量的寻址策略 ,提出一种新的Gauss基函数模糊CMAC网络 (GFCMAC) ,并进一步在对Kohonen的自组织映射算法进行改进的基础上 ,提出了GFCMAC的结构自组织算法 (SOGFCMAC) .仿真结果表明 ,采用Gauss基函数和模糊技术可以显著提高CMAC算法的非线性逼近能力 ,与传统CMAC、广义基函数CMAC和FCMAC等算法相比 ,SOGFCMAC算法在收敛速度、逼近精度和结构自组织等多方面都具有明显的优越性 .

New Structural Self-Organizing Fuzzy CMAC with Basis Functions

To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing.