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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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Proceedings ArticleDOI
Precision Onboard Small Sensor System for Unmanned Air Vehicle Testing and Control
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An examination of the ARX as a residual generator for damage detection
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Dissertation
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Proceedings ArticleDOI
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Parisa Asadollahi,Jian Li +1 more
TL;DR: A comprehensive statistical analysis of the modal properties including natural frequencies, damping ratios and mode shapes of the monitored cable-stayed bridge shows the long-term statistical structural behavior of the bridge which serves as the basis for Bayesian statistical updating for the numerical model.
Journal ArticleDOI
A stochastic unknown input realization and filtering technique
Dan Yu,Suman Chakravorty +1 more
TL;DR: In this paper, an autoregressive (AR) model based unknown input realization technique was proposed to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm.