Young-Keun Bang

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In many cases, the k-means clustering algorithm has been most frequently used to the field of data mining, fuzzy control systems and prediction since it was designed in simple procedures and excellent ability of classification. However, it sometimes brought about the failed results for non-linear data by classification behavior caused by just considering(More)
In general, it is difficult to predict non-stationary or chaotic time series since there exists drift and/or non-linearity as well as uncertainty in them. To overcome this situation, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. The proposed method(More)
In this paper, a multiple prediction system using T-S fuzzy model is presented for time series forecasting. To design predictors with better performance especially for chaos or nonlinear time series, difference data were used as their input, because they reveal the statistical patterns and the regularities concealed in time series more effectively than the(More)
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