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An Improved Local Weighted Linear Prediction Model for Chaotic Time Series |
| QU Jian-Ling1, WANG Xiao-Fei1**, QIAO Yu-Chuan2, GAO Feng1, DI Ya-Zhou1 |
1Department of Control, Naval Aeronautical Engineering Institute Qingdao Branch, Qingdao 266041
2Department of Radiology, Leiden University Medical Center, 2300 RC Leiden, Netherlands
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| Cite this article: |
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QU Jian-Ling, WANG Xiao-Fei, QIAO Yu-Chuan et al 2014 Chin. Phys. Lett. 31 020503 |
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Abstract Previous research working on local prediction state with some unsuitable neighbor points (such as false points and pseudo-false neighbor points) are the main source of errors of local prediction and these unsuitable neighbors cannot be eliminated entirely. Therefore, an improved local weighted linear prediction model based on local integrated correlation, which can reduce the influence of the residual unsuitable neighbors, is proposed to predict chaotic time series in our study. Simulation results show that the performance of the improved model is superior to the other local prediction models in the prediction of chaotic time series without and with additive white Gaussian noise.
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Received: 30 September 2013
Published: 28 February 2014
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| PACS: |
05.45.-a
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(Nonlinear dynamics and chaos)
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