EFNN——一种增强型模糊神经网络

An enforced fuzzy neural networks

提出了一种较为广义的增强型模糊神经网络 ,以达到更高的非线性系统逼近能力 .该网络模糊规则的结论以函数形式给出 ,从而决定了网络的结构由两个子网络组成 ,即特征网络和功能网络 .网络采用梯度算法来修正网络的参数 .仿真表明 :该网络具有较强的非线性逼近能力和较快的学习速度 .

Presents the general enforced fuzzy neural network (EFNN) proposed to obtain higher accuracy of closing in on nonlinear system with the consequent fuzzy rules given in the form of function, which determines that the structure of network is a combination of two sub networks. Character network and function one, and the parameters are tuned with the grade descending algorithms and concludes from simulation results that the network has a higher accuracy of closing in on nonlinear system and a faster training speed.