Robust Identification of Nonlinear Systems With Missing Observations: The Case of State-Space Model Structure

Yang, Xianqiang; Liu, Xin; Yin, Shen

doi:10.1109/tii.2018.2871194

Cited by 24 publications

(12 citation statements)

References 27 publications

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“…The EM algorithm is actually an iterative optimization algorithm and it can provide precise maximum likelihood estimates (MLE) of the unknown model parameters [6], [8], [13], [22], [29]. Compared with the conventional MLE methods, the EM algorithm can still work smoothly and provide efficient parameter estimates when dealing with the hidden variable cases [6], [8], [13]. Therefore, the EM algorithm has been widely applied in the field of system identification.…”

Section: Derivations Of the Developed Identification Methods A Bmentioning

confidence: 99%

“…In practical industries, it is often high-cost to figure out the inborn mechanism of a certain system, especially the large-scale or complicated systems [4], [11]. But in the procedure of SI, this step is saved and the process model can be recovered from the collected process data under the pre-chosen principles [1], [6], [8], [9]. That makes the SI much easier for practical implementation, therefore various approches for SI with different model structures have been proposed in the existing publications…”

Section: Introductionmentioning

confidence: 99%

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Robust Identification Method for LPV ARX Systems and its Application to a Mechanical Unit

2019

Self Cite

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This paper introduces a robust identification solution for the linear parameter varying Autoregressive Exogenous systems with outlier-contaminated outputs. The Laplace distribution with heavy tails and the expectation maximization algorithm are combined to build the robust system identification framework. To overcome the obstacles brought by the outliers, the Laplace distribution which can be decomposed into infinite Gaussian components, is applied to mathematically model the system noise. The problem of parameter estimation is solved using the expectation maximization algorithm, and the equations to infer the system model and noise parameters are simultaneously provided in the developed identification method. Finally, the verification tests performed on a numerical example and a mechanical unit are used to prove the validity of the developed identification method.

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Section: Derivations Of the Developed Identification Methods A Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Identification Method for LPV ARX Systems and its Application to a Mechanical Unit

2019

Self Cite

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“…Following mathematical determination of the VDP curve, the unknown parameters (A, B, C) need to be solved for subsequent use in engineering. A statistical learning method is a significant way to identify the parameters [26][27][28][29], and the essence of parameter solution in the VDP curve is a nonlinear least squares problem that can be solved by the common Levenberg-Marquardt (L-M) algorithm. [30][31][32][33][34][35][36][37][38] The core idea of this solution is to linearize the nonlinear function, then imitate the least squares method for this linear function.…”

Section: Geofluidsmentioning

confidence: 99%

In Situ Monitoring and Numerical Experiments on Vertical Deformation Profiles of Large-Scale Underground Caverns in Giant Hydropower Stations

Liu

Wang

et al. 2021

Geofluids

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Spatial response is a feature of rock deformation in regions surrounding large-scale underground caverns and includes significant vertical components due to the construction method of layered excavation. This vertical response is different to the longitudinal response of excavation deformation surrounding shallow tunnels. The study of longitudinal deformation profiles (LDPs), which describe the spatial response of longitudinal tunnel excavation and surrounding rock deformation, is a mature field. However, there has been no independent discussion of the relationship between vertical excavation and the spatial response of deformation in large-scale underground caverns nor the incremental characteristics of layered excavation. In this paper, we define the attenuation function λ x of unloading strength based on theoretical analyses and numerical simulations. We also propose the concept and form of the vertical deformation profile (VDP) curve for the first time and apply it to the Baihetan and Lianghekou Hydropower Stations. After fitting the complete VDP curve with a Levenberg-Marquardt algorithm, we verify its validity by comparing predicted data with in situ monitoring data. The curve can be used to quantitatively analyze the relationship between layered excavation and incremental deformation of surrounding rock, providing a basis for the rapid evaluation of staged deformation during the excavation of large underground caverns. This study has practical significance for the control of deformation in rock surrounding excavations and decision-making during the construction progress.

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“…However, the presence of missing data samples in industrial datasets can not be denied, and suitable actions are required to be taken, which may require some added computation burden. The interested readers are referred to the excellent work in [34] and references therein to work in this direction.…”

Section: Problem Descriptionmentioning

confidence: 99%

A Novel Iterative System Identification and Modeling Scheme With Simultaneous Time-Delay and Rational Parameter Estimation

Sharma

Padhy

2020

IEEE Access

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In this work, a new method is proposed to identify systems in the form of first and secondorder continuous time-delayed (CTD) models where time-delay and rational parameters are estimated simultaneously. The time delay is explicitly brought into system parameters by discretizing the CTD models. The sampled input-output data from the system is used in an iterative prediction error minimization (PEM) algorithm to identify the discrete-time delayed (DTD) model from which the equivalent CTD model is extracted. The DTD model includes the fractional and integer part of time-delay, where the phase contribution due to the fractional part is used to correct the estimation of delay as well as the termination of the algorithm until the phase contribution lies within one sample time. The efficacy of the proposed method is demonstrated by the simulation study with noisy measurements for four different systems along with the experimental validation.

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Robust Identification of Nonlinear Systems With Missing Observations: The Case of State-Space Model Structure

Cited by 24 publications

References 27 publications

Robust Identification Method for LPV ARX Systems and its Application to a Mechanical Unit

Robust Identification Method for LPV ARX Systems and its Application to a Mechanical Unit

In Situ Monitoring and Numerical Experiments on Vertical Deformation Profiles of Large-Scale Underground Caverns in Giant Hydropower Stations

A Novel Iterative System Identification and Modeling Scheme With Simultaneous Time-Delay and Rational Parameter Estimation

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