Presentation | 2001/3/10 One selecting near neighbors for local linear approximation methods Tomoya SUZUKI, Tohru IKEGUCHI, Masuo SUZUKI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Local linear approximation methods are one of the common tools for nonlinear prediction of chaotic time series. In the case of predicting time series data corrupted by observation noise with this method, it is possible that unsuitable points are selected as spurious neighbors, which causes lower prediction accuracy. To solve this issue, we propose a simple noise reduction method, which is based on the selection of suitable near neighbors with observational noise. In order to evaluate the proposed method, we apply local linear approximation methods to the Henon map corrupted by noise. As a result, we confirm that the noise reduction method works well to improve prediction accuracy. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | local linear approximation / prediction of chaotic time series / searching near neighbors / noise reduction |
Paper # | NLP2000-174 |
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Committee | NLP |
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Conference Date | 2001/3/10(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | One selecting near neighbors for local linear approximation methods |
Sub Title (in English) | |
Keyword(1) | local linear approximation |
Keyword(2) | prediction of chaotic time series |
Keyword(3) | searching near neighbors |
Keyword(4) | noise reduction |
1st Author's Name | Tomoya SUZUKI |
1st Author's Affiliation | Graduate School of Science, Science University of Tokyo() |
2nd Author's Name | Tohru IKEGUCHI |
2nd Author's Affiliation | Graduate School of Science and Technology, Saitama University |
3rd Author's Name | Masuo SUZUKI |
3rd Author's Affiliation | Department of Applied Physics, Faculty of Science, Science University of Tokyo |
Date | 2001/3/10 |
Paper # | NLP2000-174 |
Volume (vol) | vol.100 |
Number (no) | 681 |
Page | pp.pp.- |
#Pages | 8 |
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