Nonparametric identification of nonlinear dynamic systems
A nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentat...
保存先:
| 第一著者: | |
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| フォーマット: | 電子媒体 図書の章 |
| 言語: | 英語 |
| 出版事項: |
KIT Scientific Publishing
2018
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| シリーズ: | Schriftenreihe des Instituts für Technische Mechanik, Karlsruher Institut für Technologie
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| 主題: | |
| オンライン･アクセス: | DOAB: download the publication DOAB: description of the publication |
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| 要約: | A nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentation technique that turns the filter into a dual state and parameter estimator, of which an extension towards nonparametric identification is proposed in the present work. |
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| 物理的記述: | 1 electronic resource (XXVIII, 194 p. p.) |
| ISBN: | KSP/1000085419 9783731508342 |
| アクセス: | Open Access |