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Identification of Dynamic Systems An introduction with Applications Rolf Isermann, Marco Münchhof

By: Isermann, Rolf.
Contributor(s): Münchhof, Marco.
New York Springer 2011Description: xxv, 705 páginas : ilustraciones 24 cm.ISBN: 9783540788782.Subject(s): System identification -- Mathematical models | Identificación del sistema moderno matemático | Sistemas dinamicosDDC classification: 003.1
Contents:
Theoretical and Experimental Modeling – Tasks and Problems for the Identification of Dynamic Systems – Mathematical Models of Linear Dynamic Systems and Stochastic Signals – Identification of Non-Parametric Models in the Frequency Domain- Continuous Time Signals – Spectral Analysis Methods for Periodic and Non-Periodic Signals – Frequency Response Measurement for Periodic Test Signals – Identification of Non-Parametric Models with Correlation Analysis- Continuous and Discrete Time – Correlation Analysis with Continuous Time Models – Correlation Analysis with Discrete time Models – Identification with Parametric Models- Discrete Time Signals – Identification with Parametric Models- Continuous Time Signals – Identification of Multi-Variable Systems – Identification of Multi-Variable Systems – Identification of Non-Linear Systems – Iterative Optimization – Neural Networks and Lookup Tables for Identification – State and Parameter Estimation by Kalman Filtering – Miscellaneous Issues – Numerical Aspects – Practical Aspects of Parameter Estimation – Applications –Appendix --
List(s) this item appears in: Maestría Complejidad
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Incluye referencias bibliográficas e índice.

Theoretical and Experimental Modeling – Tasks and Problems for the Identification of Dynamic Systems – Mathematical Models of Linear Dynamic Systems and Stochastic Signals – Identification of Non-Parametric Models in the Frequency Domain- Continuous Time Signals – Spectral Analysis Methods for Periodic and Non-Periodic Signals – Frequency Response Measurement for Periodic Test Signals – Identification of Non-Parametric Models with Correlation Analysis- Continuous and Discrete Time – Correlation Analysis with Continuous Time Models – Correlation Analysis with Discrete time Models – Identification with Parametric Models- Discrete Time Signals – Identification with Parametric Models- Continuous Time Signals – Identification of Multi-Variable Systems – Identification of Multi-Variable Systems – Identification of Non-Linear Systems – Iterative Optimization – Neural Networks and Lookup Tables for Identification – State and Parameter Estimation by Kalman Filtering – Miscellaneous Issues – Numerical Aspects – Practical Aspects of Parameter Estimation – Applications –Appendix --

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