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
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TL;DR: This work proposes a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models.
Abstract: We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.
118 citations
TL;DR: In this paper, a data-driven method for the approximation of the Koopman generator called gEDMD is proposed, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition).
118 citations
13 Dec 1995
TL;DR: In this article, the authors show that the multivariable output-error state-space model (MOESP) class of subspace model identification (SMI) schemes can be extended to identify Wiener systems, a series connection of a linear dynamic system followed by a static nonlinearity.
Abstract: In this paper we show that the multivariable output-error state-space model (MOESP) class of sub-space model identification (SMI) schemes can be extended to identify Wiener systems, a series connection of a linear dynamic system followed by a static nonlinearity. In this paper, we restrict to present these extensions for the case the Taylor series expansion of the static nonlinearity contains odd terms. It is shown that the extension allows to identity the linear part of the Wiener systems as if the static nonlinearity is not present. In this way, it is related to cross-correlation analysis techniques.
118 citations
TL;DR: The goal of this paper is to design a statistical test for the camera model identification problem based on the heteroscedastic noise model, which more accurately describes a natural raw image.
Abstract: The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the heteroscedastic noise model, which more accurately describes a natural raw image. This model is characterized by only two parameters, which are considered as unique fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performances are theoretically established. For a practical use, two generalized LRTs are designed to deal with unknown model parameters so that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural raw images highlight the relevance of the proposed approach.
118 citations
TL;DR: Differential evolution (DE) is an optimization method developed to perform direct search in a continuous parameter space without requiring any derivative estimation as discussed by the authors, which can be used to identify the parameter values relevant to its application.
118 citations