Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: An overview and critical analysis of the state of the art in this sector are proposed and the main contributions to model-based experiment design procedures in terms of novel criteria, mathematical formulations and numerical implementations are highlighted.
650 citations
TL;DR: In this article, the authors present several methods for constitutive parameter identification based on kinematic full-field measurements, namely the finite element model updating method (FEMU), the constitutive equation gap method (CEGM), the virtual fields method (VFM), the EGM, the equilibrium gap method, and the reciprocity gap method.
Abstract: This article reviews recently developed methods for constitutive parameter identification based on kinematic full-field measurements, namely the finite element model updating method (FEMU), the constitutive equation gap method (CEGM), the virtual fields method (VFM), the equilibrium gap method (EGM) and the reciprocity gap method (RGM) Their formulation and underlying principles are presented and discussed These identification techniques are then applied to full-field experimental data obtained on four different experiments, namely (i) a tensile test, (ii) the Brazilian test, (iii) a shear-flexural test, and (iv) a biaxial test Test (iv) features a non-uniform damage field, and hence non-uniform equivalent elastic properties, while tests (i), (ii) and (iii) deal with the identification of uniform anisotropic elastic properties Tests (ii), (iii) and (iv) involve non-uniform strain fields in the region of interest
645 citations
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28 May 2007TL;DR: In this paper, the authors present an approach for the estimation of spectra and frequency response functions based on output-error parametric model estimation and subspace model identification with random variables and signals.
Abstract: Preface 1. Introduction 2. Linear algebra 3. Discrete-time signals and systems 4. Random variables and signals 5. Kalman filtering 6. Estimation of spectra and frequency response functions 7. Output-error parametric model estimation 8. Prediction-error parametric model estimation 9. Subspace model identification 10. The system identification cycle Notation and symbols List of abbreviations References Index.
643 citations
TL;DR: The subspace-based approach is found to perform competitive with respect to prediction-error methods, provided the system is properly excited.
627 citations
TL;DR: In this paper, the authors present two algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data, which are classified as one of the subspace model identification schemes.
Abstract: In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...
624 citations