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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.


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TL;DR: In this paper, the Extended Kalman Filter (EKF) was applied to model identification of DO-BOD interaction in a freshwater river system based on daily field data, and it was found that the basic model structure, as defined by a dynamic version of the Streeter-Phelps equations, was inadequate and, in the case of the river considered, it was necessary to introduce additional sustained sunlight terms to account for the effects of floating algal populations.
Abstract: The Extended Kalman Filter (EKF) provides a logical statistically based extension to those existing approaches to model fitting based on deterministic model response error (surface) minimization. Its crucial feature as a basis for identification, however, is the recursive nature of the algorithm which permits the estimation of possible variations in the model parameters. Depending upon whether such estimated variations are realistic or not, bearing in mind the physical nature of the dynamic system, it is possible to formulate criteria for model adequacy. This approach to model identification was applied to the problem of modeling DO-BOD interaction in a freshwater river system based on daily field data. It was found that the basic model structure, as defined by a dynamic version of the Streeter-Phelps equations, was inadequate and, in the case of the river considered, it was necessary to introduce additional sustained sunlight terms to account for the effects of floating algal populations.

112 citations

Journal ArticleDOI
TL;DR: In this paper, a recursive least squares (RLS) estimation method based on the auxiliary model identification idea and the decomposition technique is presented for pseudo-linear system identification with missing data, and an interval-varying RLS algorithm is derived for estimating the system parameters.
Abstract: This study focuses on the parameter identification problems of pseudo-linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique. First, an auxiliary model-based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition-based RLS algorithm is presented. Then for the system identification with missing data, an interval-varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval-varying RLS algorithm and introduces the forgetting factors to track the time-varying parameters. The simulation results show that the proposed algorithms can work well.

112 citations

Journal ArticleDOI
Er-Wei Bai1
TL;DR: In this article, the linear part of a Hammerstein model is decoupled from the nonlinear part in model identification, and the identification of the linear parts becomes a linear problem and accordingly enjoys the same convergence and consistency results as if the unknown nonlinearity is absent.

112 citations

Journal ArticleDOI
TL;DR: A hierarchical least squares algorithm is presented for the Hammerstein-Wiener system by using the auxiliary model identification idea and the hierarchical identification principle and it is shown that the proposed hierarchical identification approach is computationally more efficient than the existing over-parametrization method.
Abstract: This letter focuses on identification problems of a Hammerstein-Wiener system with an output error linear element embedded between two static nonlinear elements. A hierarchical least squares algorithm is presented for the Hammerstein-Wiener system by using the auxiliary model identification idea and the hierarchical identification principle. The major contributions of the present study are that the identification model is formulated by using the auxiliary model identification idea (the estimate of the unknown internal variable is replaced with the output of an auxiliary model) and that the bilinear parameter vectors in the identification model are estimated by using the hierarchical identification principle. The proposed hierarchical identification approach is computationally more efficient than the existing over-parametrization method.

112 citations

Journal ArticleDOI
TL;DR: The machinery of neural networks is proposed as a tool to accomplish the identification process of the classical Preisach-type hysteresis model and a comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.
Abstract: The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.

112 citations


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