基于模糊分类的模糊神经网络辨识方法及应用

基于改进的T-S模型，提出一种自适应模糊神经网络模型(AFNN)，给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁，逼近能力强，能够显著提高辨识精度，并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中，仿真结果验证了该方法的有效性，表明该网络能够实现复杂非线性系统的建模，而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。

Fuzzy Neural Network Identification Method Based on Fuzzy Clustersand its Application

In accordance with modified T-S model,an adaptive fuzzy neural network(AFNN) model is proposed. The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained. The parameters of AFNN are on-line identified by means of Kalman filtering algorithm. The proposed AFNN has the simple model structure and the ability of universal approach, and improves greatly the precision of identification. The identified fuzzy model has the advantages of simplicity and effectiveness. The AFNN is applied to the fuzzy identification for a nonlinear system and CSTR.The simulation results show the effectiveness of the proposed method.