Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: This article develops a new framework in order to work with data that are not necessarily persistently exciting, and investigates necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems.
Abstract: The use of persistently exciting data has recently been popularized in the context of data-driven analysis and control. Such data have been used to assess system-theoretic properties and to construct control laws, without using a system model. Persistency of excitation is a strong condition that also allows unique identification of the underlying dynamical system from the data within a given model class. In this article, we develop a new framework in order to work with data that are not necessarily persistently exciting. Within this framework, we investigate necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems. For certain analysis and design problems, our results reveal that persistency of excitation is not necessary. In fact, in these cases, data-driven analysis/control is possible while the combination of (unique) system identification and model-based control is not. For certain other control problems, our results justify the use of persistently exciting data, as data-driven control is possible only with data that are informative for system identification.
190 citations
Journal Article•
TL;DR: In this paper, a blind approach to the sampled Hammerstein-Wiener model identification is proposed, where no a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
Abstract: In this paper, we propose a blind approach to the sampled Hammerstein-Wiener model identification. By using the blind approach, it is shown that all internal variables can be recovered solely based on the output measurements. Then, identification of linear and nonlinear parts can be carried out. No a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
190 citations
TL;DR: It is concluded that in spite of the utility of the standard discrete convolution approach used in statistical parametric maps (SPM), nonlinear BOLD phenomena and unspecific input temporal sequences must be included in the fMRI analysis.
190 citations
TL;DR: A Wiener system, i.e., a system in which a linear dynamic part is followed by a nonlinear and memoryless one, is identified and a nonparametric algorithm recovering the characteristic from input-output observations of the whole system is proposed.
Abstract: A Wiener system, i.e., a system in which a linear dynamic part is followed by a nonlinear and memoryless one, is identified. No parametric restriction is imposed on the functional form of the nonlinear characteristic of the memoryless subsystem, and a nonparametric algorithm recovering the characteristic from input-output observations of the whole system is proposed. Its consistency is shown and the rate of convergence is given. An idea for identification of the impulse response of the linear subsystem is proposed. Results of numerical simulation are also presented. >
190 citations
TL;DR: The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context.
189 citations