一种基于T-S模糊模型的自适应建模方法及其应用

An Adaptive Modeling Method Based on T-S Fuzzy Models and Its Application

摘要: 提出了一种改进的基于T-S模糊RBF神经网络模型的辨识算法和自适应方法，采用模糊C均值聚类(FCM)算法划分输入输出数据空间，最后将该算法应用于丙烯腈收率的预报，仿真结果表明了这种基于T-S模糊模型的自适应建模方法的有效性。

Abstract: On the basis of partitioning the input-output space using FCM method, a modified algorithm of parameters identification and adaptation is presented based on the T-S fuzzy RBF neural network model. This model is used to predict yield of acrylonitrile and the simulation results demonstrate the effectiveness of this new adaptive T-S fuzzy model.