On-Line Clustering for Nonlinear System Identification Using Fuzzy Neural Networks
Wen Yu, Andrés Ferreyra-Ramírez · 2005
In this paper we propose a novel on-line clustering approach which can be applied for nonlinear system identification. Both structure and parameters of fuzzy neural networks are updated on-line. The new clustering method for the structure identification can divide input/output data into different groups (rule number) by on-line data. For the parameter learning, our algorithm has two advantages over the others. First, the normal methods for parameter identification are based on a fixed structure and whole data, for example ANFIS by C. F. Jang and C. Teng Lin (1998), but after clustering we know each group corresponds to one rule, so we train each rule by its group data, it is more effective. Second, we give a time-varying learning rate for the common used backpropagation algorithm, we prove that the new algorithm is stable and faster than backpropagation algorithm