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论文类型：期刊论文

发表时间：2011-07-01

发表刊物：ASIAN JOURNAL OF CONTROL

收录刊物：Scopus、SCIE、EI

卷号：13

期号：4

页面范围：576-581

ISSN号：1561-8625

关键字：Singular value decomposition; Kalman filtering algorithm; GK fuzzy clustering; chaotic time series

摘要：The main purpose of this paper is to study a new method to model and predict a chaotic time series using a fuzzy model. First, the GK fuzzy clustering method is used to confirm the input space of the fuzzy model. The goal is to divide the training patterns into representative groups so that patterns within one cluster are more similar than those belonging to other clusters. Then, the Kalman filtering algorithm with singular value decomposition is applied to estimate the consequent parameters of the fuzzy model in order to avoid error delivery and error accumulation. The effectiveness of the proposed method is evaluated through simulated examples, including Mackey-Glass time series and Lorenz chaotic systems. The results show that the proposed method provides effective and accurate prediction.