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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bang, Y.K. Lee, C.H. |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of electrical and electronics engineering, Hyojadong 192-1, Chunchon, Kangwon National University, Korea (Bang, Y.K.; Lee, C.H.) |
| Abstract | 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 original data can. The proposed method consists of three major procedures. First, multiple model fuzzy predictors (MMFPs) are constructed based on the optimal difference candidates. Next, an adaptive drive mechanism (ADM) based on rough sets is designed for the selection of the best one among the multiple predictors according to each input data. Finally, an error compensation mechanism (ECM) based on the cross-correlation analysis is suggested in order to enhance further the prediction performances. Also we show the effectiveness of the proposed method by computer simulation for the various typical time series. |
| Starting Page | 1914 |
| Ending Page | 1919 |
| File Size | 968226 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424435968 |
| ISSN | 10987584 |
| DOI | 10.1109/FUZZY.2009.5277403 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-20 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Rough sets Predictive models Chaos Fuzzy systems Time series analysis Set theory Pattern analysis Error compensation Electrochemical machining |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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