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: In this paper, a deterministic approach based on separable least squares (SLS) is proposed for the identification of systems with input nonlinearities of known structure, where the identification problem is shown to be equivalent to a one dimensional minimization problem.
Abstract: This paper studies identification of systems with input nonlinearities of known structure. For input nonlinearities parameterized by one parameter, a deterministic approach is proposed based on the idea of separable least squares. The identification problem is shown to be equivalent to a one-dimensional minimization problem. The method is very effective for several common static and non-static input nonlinearities. For a general input nonlinearity, a correlation analysis based identification algorithm is presented which is shown to be convergent.
224 citations
TL;DR: A damping parameter estimation algorithm for dynamical systems based on the sine frequency response is proposed and a damping factor is introduced in the proposed iterative algorithm in order to overcome the singular or ill-conditioned matrix during the iterative process.
224 citations
TL;DR: A systematic overview of basic research on model selection approaches for linear-in-the-parameter models, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design is presented.
Abstract: The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.
223 citations
TL;DR: The present study carries out output-only modal analysis using two blind source separation techniques, namely independent component analysis and second-order blind identification using the concept of virtual source.
223 citations
TL;DR: A solution to the problem of identifying multivariable finite dimensional linear time-invariant systems from noisy input/output measurements is developed in the framework of subspace identification and it is shown that the proposed algorithms give consistent estimates when the system is operating in open- or closed-loop.
223 citations