基于递阶模糊聚类的混沌时间序列预测

Prediction of chaotic time series based on hierarchical fuzzy-clustering

提出一种新的基于递阶模糊聚类系统的模糊建模方法.目的在于通过一系列的步骤优化T-S模糊模型结构,实现非线性系统的建模和预测.首先利用最近邻聚类法初始划分输入空间,得到规则数及初始聚类中心,用模糊C均值算法(FCM)进一步优化聚类中心;然后利用加权最小二乘法估计模糊模型的初始参数,进一步利用带遗忘因子的递推最小二乘法优化结论参数.采用该方法对Mackey-Glass混沌时间序列进行预测实验,结果表明可以对Mackey-Glass混沌时间序列进行准确建模和预测,证明了本方法的有效性.

The paper introduces a new method for fuzzy modeling based on a hierarchical fuzzy-clustering scheme. The method consists of a sequence of steps aiming at developing a Takagi-Sugeno （TS） fuzzy model of optimal structure. The premise parameters＇ identification consists of two steps： Start 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; then premise parameters are further processed using a fuzzy C-means algorithm （FCM）. The conclusion parameters are identified by the weighted least square method and further optimized by selective recursive least square method. To illustrate the performance of the proposed method, simulations on chaotic Mackey- Glass time series prediction are performed. The results show that the chaotic Mackey-Glass time series are accurately predicted, which demonstrates the effectiveness of this method.