Open AccessBook
Applied system identification
Reads0
Chats0
TLDR
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
More filters
Journal ArticleDOI
On the use of Pade approximants in the estimation of eigenfrequencies and damping ratios of a vibrating system
TL;DR: In this paper, an approach to estimate eigenfrequencies and damping ratios of a vibrating system, in time domain from output data only, is studied based on the interpretation of histograms obtained from the poles of Pade approximants.
Damage Detection Using Controllability Grammian Matrices
Douglas Domingues Bueno,Clayton Rodrigo Marqui,Antonio Eduardo Turra,Vicente Lopes Junior,Greek Symbols +4 more
TL;DR: In this article, the authors proposed to locate the damage through controllability analysis, which is based on the control grammian matrix, and it should be advantageous for practical structural health monitoring (SHM) in cases where the number of available sensors is small.
Book ChapterDOI
Information-Dependent switching of identification criteria in a genetic programming system for system identification
TL;DR: The reasons for the failure of the GP approach are explained, a solution strategy for improving performance is presented and using more than one identification criterion (fitness function) and switching based on the information content of the data enable standard GP algorithms to find better solutions in shorter times.
Journal ArticleDOI
Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data
TL;DR: In this article, a methodology for design of Kalman filter, using interval type-2 fuzzy systems, in discrete time domain, via spectral decomposition of experimental data, is proposed.