一种神经模糊系统模型  

A NEURAL FUZZY SYSSTEM MODEL

作　　者：孙怀江[1] 杨静宇[1] 沈俊 

机构地区：[1]南京理工大学计算机科学系,南京210094 [2]波尔多第三大学图像实验室

出　　处：《计算机学报》1998年第S1期121-126,共6页Chinese Journal of Computers

摘　　要：本文提出一种神经模糊系统模型，其中模糊规则前件用л隶属函数（形状类似于三角形隶属函数，但具有平滑性）表达，给出了类似于BP的参数学习算法．对于平滑函数近似问题的仿真结果表明，与模糊规则前件使用三角形隶属函数的神经模糊系统模型相比，本文提出的模型具有学习过程更加稳定平滑和逼近误差小的优点．对这两种模型性能上的差异做了定性解释．A neural fuzzy system model where the antecedents of fuzzy rules are expressed bymembership functions (similar to trangular membership functions but smooth )is proposed and itsparameter learning algorithm like the BP is derived in this paper Simulation results for smoothfunction approximation problem indicate that the learning processes are more stable and smooth andthe approximation errors are more small for the proposed model compared to the model using thetriangular membership functions Performace differences between the two models are explainedqualitatively

关 键 词：神经模糊系统 隶属函数 学习算法 平滑 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]