Fuzzy Prediction of Time Series Based on Kalman Filter with SVD Decomposition

The fuzzy modeling method with singular value decomposition (SVD) is proposed in the paper. First of all, the fuzzy clustering is utilized to define the input space of fuzzy model. In addition, the recursive Kalman filtering algorithm with singular value decomposition is used to confirm the conclusion parameters of fuzzy model for the sake of accumulating and transferring of the errors. The parameters of fuzzy model are optimized on the basis of the presented algorithm. To illustrate the performance of the proposed method, simulations on the chaotic Mackey-Glass time series prediction are performed. The simulating results can show that the chaotic Mackey-Glass time series are accurately predicted, and demonstrate the effectiveness.