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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/4898
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| タイトル: | Detecting Switch Dynamics in Chaotic Time-Waveform using a Parametrized Family of Nonlinear Predictors |
| 著者: | Tokuda, Isao
Tokunaga, Ryuji
Matsumoto, Takashi |
| キーワード: | chaos
bifurcation
chaotic time-waveform
switch dynamics
non-stationarity
parametrized family of nonlinear predictors |
| 発行日: | 2000-01-01 |
| 出版者: | Elsevier |
| 誌名: | Physica D : Nonlinear Phenomena |
| 巻: | 135 |
| 号: | 1-2 |
| 開始ページ: | 63 |
| 終了ページ: | 78 |
| DOI: | 10.1016/S0167-2789(99)00124-4 |
| 抄録: | An algorithm is presented for detecting switch dynamics in chaotic time-waveform. By the “switch dynamics,” we mean that the chaotic time-waveform is measured from a dynamical system whose bifurcation parameters are occasionally switched among a set of slightly different parameter values. First, the switched chaotic time-waveform is divided into windows of short-term timewaveforms. From the set of windowed time-waveforms, “qualitatively similar” parametrized family of nonlinear predictors is constructed. “Qualitatively similar” parametrized family means that the family of nonlinear predictors exhibits “qualitatively similar” bifurcation phenomena as the original. By characterizing the windows of short-term chaotic time-waveforms in terms of the “qualitative” parameters of nonlinear predictors, switch dynamics of their associated bifurcation parameters are detected. For the Lorenz equations, the R¨ossler equations, and the Mackey-Glass equations, efficiency of the algorithm is demonstrated. In the experiment, chaotic time-waveforms contaminated with observational noise is considered. |
| Rights: | NOTICE: This is the author's version of a work accepted for publication by Elsevier. Isao Tokuda, Ryuji Tokunaga and Takashi Matsumoto, Physica D : Nonlinear Phenomena, 135(1-2), 2000, 63-78, http://dx.doi.org/10.1016/S0167-2789(99)00124-4 |
| URI: | http://hdl.handle.net/10119/4898 |
| 資料タイプ: | author |
| 出現コレクション: | b10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
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記述 |
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| C6283.pdf | | 467Kb | Adobe PDF | 見る/開く |
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