Identification of Two-Dimensional Causal Systems With Missing Output Data via Expectation–Maximization Algorithm

Chen, Jing; Huang, Biao; Ding, Feng

doi:10.1109/tii.2020.3025581

Cited by 17 publications

(3 citation statements)

References 40 publications

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“…System identification is studying the theory and methods of establishing the mathematical models of systems. [1][2][3][4][5] In recent years, the identification of for nonlinear systems has been applied to various areas. For a typical nonlinear system, the block-oriented nonlinear model which consists of a nonlinear static block and a linear dynamic block is used to describe the complex characteristics of industrial processes.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

2023

Adaptive Control & Signal

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Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.

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Section: Introductionmentioning

confidence: 99%

“…System identification is studying the theory and methods of establishing the mathematical models of systems 1‐5 . In recent years, the identification of for nonlinear systems has been applied to various areas.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

2023

Adaptive Control & Signal

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“…Its basic idea is to estimate the hidden variables in the E step based on the estimated parameters, and then to update the parameters in the M step according to the estimated hidden variables, 26 these two steps iteratively run until the hidden variables and parameters both converge to their true values. [27][28][29][30] Those hidden variables include: time-delays, 31 missing data, 32 and model identities. 33 Inspired by the above methods, an expectation maximization algorithm (EM) is proposed for SOH model in this paper.…”

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confidence: 99%

Expectation Maximization Algorithm for a Battery State of Health Model with Missing Data

Han

2023

J. Electrochem. Soc.

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With the popularity of lithium-ion batteries, battery state of health (SOH) estimation has become one of the current research hotspots. Due to network congestion, collected data usually encounter time-delay or packet loss. In this paper, an expectation maximization (EM) algorithm is proposed for the SOH model which is approximated by a polynomial model. Based on the EM method, the missing data are computed in the E step, and the parameters are updated in the M step. Compared with the least square method, the proposed algorithm has more accurate estimation accuracy. The simulation example shows the effectiveness of the proposed algorithm.

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The Maximum Correntropy Criterion-Based Robust Hierarchical Estimation Algorithm for Linear Parameter-Varying Systems with Non-Gaussian Noise

Wang

2022

Circuits Syst Signal Process

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Identification of Two-Dimensional Causal Systems With Missing Output Data via Expectation–Maximization Algorithm

Cited by 17 publications

References 40 publications

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Expectation Maximization Algorithm for a Battery State of Health Model with Missing Data

The Maximum Correntropy Criterion-Based Robust Hierarchical Estimation Algorithm for Linear Parameter-Varying Systems with Non-Gaussian Noise

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