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System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.


Papers
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Journal ArticleDOI
TL;DR: The proposed cancellation method allows to obtain non asymptotic estimators for the unknown coefficients of continuous-time systems with structured entries and application to delayed and switching hybrid systems.

100 citations

Journal ArticleDOI
TL;DR: In this paper, a data-based approach for building a prediction model consisting of feature generation, feature selection and model identification and validation steps is proposed, where a multivariable linear regression models are used in predictions.
Abstract: The aim of this study is to predict residual stress and hardness of a case-hardened steel samples based on the Barkhausen noise measurements. A data-based approach for building a prediction model proposed in the paper consists of feature generation, feature selection and model identification and validation steps. Features are selected with a simple forward-selection algorithm. A multivariable linear regression models are used in predictions. Throughout the selection and identification procedures a cross-validation is used to guarantee that the results are realistic and hold also for future predictions. The obtained prediction models are validated with an external validation data set. Prediction accuracy of the prediction models is good showing that the proposed modelling scheme can be applied to prediction of material properties.

100 citations

Journal ArticleDOI
TL;DR: The conclusion is that 4SID can be viewed as a linear regression multistep-ahead prediction error method with certain rank constraints with certainRank constraints within the standard framework of system identification and linear regression estimation.

99 citations

Journal ArticleDOI
TL;DR: The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions with a focus on the adaptive observer technique.
Abstract: The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.

99 citations

Journal ArticleDOI
TL;DR: In this article, a discrete-time method for structural system identification using linear filters is presented, which is well known in electrical and systems engineering fields, and therefore is not new.
Abstract: Most of the previous studies considered structural system identification in the continuous‐time domain. The discrete‐time approach to the problem is more natural since all the recordings are in the discrete‐time form. This study presents a discrete‐time method for system identification by using discrete‐time linear filters. The method itself is well known in electrical and systems engineering fields, and therefore is not new. The objective in the paper is to present the method by emphasising its relation to the more familiar continuous‐domain modal analysis approach that is widely used in structural engineering. In addition to the method, some practical but important problems are also discussed in the paper, such as the processing of data, the selection and validation of models in the identification, and the detection of soil‐structure interaction. As an example, a 12‐story building was identified by using recordings from the magnitude 6.4, San Fernando, California earthquake of February 9, 1971.

99 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023177
2022361
2021646
2020813
2019804
2018862