Prediction of chaotic time series based on robust fuzzy clustering
基于鲁棒模糊聚类的混沌时间序列预测

We propose a new method for fuzzy modeling based on a robust fuzzy-clustering algorithm. The induced local spatial similarity improved the system's robustness to noise and outsider and predicted the robustness of the modeling system. Starting from an initial fuzzy partition of input space by a nearest-neighbor clustering method to get the number of rules and the initial clustering center, we can compute and optimize the fuzzy membership and the clustering center with a robust fuzzy-clustering algorithm and get the high precision T-S model. The obtained parameters were identified by the least square method and further optimized by selective recursive least square. The proposed method was applied to simulations of chaotic Mackey-Glass time series modeling and prediction. The results demonstrated the robustness, effectiveness and practicability of the method.