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Applied system identification
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In this paper, the authors introduce the concept of Frequency Domain System ID (FDSI) and Frequency Response Functions (FRF) for time-domain models, as well as Frequency-Domain Models with Random Variables and Kalman Filter.Abstract:
1. Introduction. 2. Time-Domain Models. 3. Frequency-Domain Models. 4. Frequency Response Functions. 5. System Realization. 6. Observer Identification. 7. Frequency Domain System ID. 8. Observer/Controller ID. 9. Recursive Techniques. Appendix A: Fundamental Matrix Algebra. Appendix B: Random Variables and Kalman Filter. Appendix C: Data Acquisition.read more
Citations
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An experimental study of a midbroken 2-bay, 6-storey reinforced concrete frame subject to earthquakes
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Proceedings ArticleDOI
Random matrix based approach to quantify the effect of measurement noise on Hankel matrix
TL;DR: This paper focuses on the development of analytical methods for uncertainty quantification of the models obtained by the Eigensystem Realization Algorithm to quantify the effect of noise in the input-output experimental data.
Journal ArticleDOI
Fault diagnosis for switching system using Observer Kalman filter IDentification
Abdelkader Akhenak,Laurent Bako,Laurent Bako,Eric Duviella,Komi Midzodzi Pekpe,Stéphane Lecoeuche,Stéphane Lecoeuche +6 more
TL;DR: In this article, the authors propose a fault detection and isolation approach based on the identification of the parameters characterizing the system without any a priori knowledge, which is then invariant to the presence of actuator or sensor faults.
Application of Singular Value Decomposition Technique to System Identification by Doping an Optimum Signal
TL;DR: In this article, the authors proposed a system identification methodology by using eigensystem realization algorithm (ERA) with a doping signal such that noise effect can be attenuated, the doping signal is obtained by integrating optimization technique with singular value decomposition (SVD) technique.