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Design of Multiple Model Fuzzy Predictors using Data Preprocessing and its Application  

Bang, Young-Keun (강원대 대학원 전기전자공학과)
Lee, Chul-Heui (강원대 전기전자공학부)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.58, no.1, 2009 , pp. 173-180 More about this Journal
Abstract
It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.
Keywords
Non-stationary Time Series; Multiple Fuzzy Model; Data Preprocessing; Model Selection; Error Compensation;
Citations & Related Records

Times Cited By SCOPUS : 4
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