An Augmented Model Approach for Identification of Nonlinear Errors-in-Variables Systems Using the EM Algorithm

Guo, Fan; Wu, Ouyang; Kodamana, Hariprasad; Ding, Yongsheng; Huang, Biao

doi:10.1109/tsmc.2017.2692273

Cited by 20 publications

(9 citation statements)

References 35 publications

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“…In this section, to re-verify the above observations, CSTR process is employed to verify the effectiveness of the BSO-EM-T algorithm. The dynamic differential models of the CSTR process are display as follows: 38,39 dC A (t)…”

Section: F I G U R E 5 𝛿(%)mentioning

confidence: 99%

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

Wang

Xiong

2023

Adaptive Control & Signal

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SummaryIn this paper, the parameter identification of bilinear state‐space model (SSM) in the presence of random outliers and time‐varying delays is investigated. Under the basis of the observable canonical form of the bilinear model, the system output can be written as a regressive form, and a bilinear state observer is applied to estimate the unknown states. To eliminate the influence of outliers and time‐varying delays on parameter estimation, we employ the Student's distribution to deal with the measurement noise and use a first‐order Markov chain to model the delays. In the framework of expectation‐maximization (EM) algorithm, the unknown parameters, delays, noise variance, states and transition probability matrix can be estimated iteratively. A numerical simulation and a continuous stirred tank reactor (CSTR) process demonstrate that the proposed algorithm has good immunity against outliers and time‐varying delays and offers good estimation accuracy for the bilinear SSM.

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Section: F I G U R E 5 𝛿(%)mentioning

confidence: 99%

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

Wang

Xiong

2023

Adaptive Control & Signal

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“…However, the complex network and laboratory analysis process also bring some identification constraints, such as the packet dropouts, the communication time-delays, the nonlinear signals and the unmeasureable sampled outputs [17]- [19]. There exist many identification methods for systems with such constraints.…”

Section: A(d)x(t) = B(d)u(t) + V(t) and The Other Is A Nonlinear Commentioning

confidence: 99%

Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

Chen

Liu

Ding

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-A stochastic gradient based particle filter algorithm is developed for an ARX model with nonlinear communication output in this paper. This non-standard ARX model consists of two submodels, one is a linear ARX model and the other is a nonlinear output model. The process outputs (outputs of the linear submodel) transmitted over a communication channel are unmeasureable, while the communication outputs (outputs of the nonlinear submodel) are available, and both of the twotype outputs are contaminated by white noises. Based on the rich input data and the available communication output data, a stochastic gradient based particle filter algorithm is proposed to estimate the unknown process outputs and parameters of the ARX model. Furthermore, a direct weight optimization method and the Epanechnikov kernel method are extended to modify the particle filter when the measurement noise is a Gaussian noise with unknown variance and the measurement noise distribution is unknown. The simulation results demonstrate that the stochastic gradient based particle filter algorithm is effective.

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“…So, if we could use the information regarding the inputs, system identification performance can be improved. Hence, in this work, we also consider an input generation dynamics [24,27] in conjunction with the EIV model. Here, we attribute the following nonlinear model to the input generation dynamics and assume that the structure of the input generation dynamics is known.…”

Section: The Robust Errors-in-variables Modelmentioning

confidence: 99%

“…Our earlier work [27] has focused on the identification of nonlinear EIV systems using the EM algorithm, nevertheless, without considering the robustness in presence of outlying measurements.…”

Section: Appendix Cmentioning

confidence: 99%

Robust identification for nonlinear errors-in-variables systems using the EM algorithm

Guo

Hariprasad

Huang

et al. 2017

Journal of Process Control

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An Augmented Model Approach for Identification of Nonlinear Errors-in-Variables Systems Using the EM Algorithm

Cited by 20 publications

References 35 publications

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

Robust identification for nonlinear errors-in-variables systems using the EM algorithm

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