Parameter estimation of Markov‐switching Hammerstein systems using the variational Bayesian approach

Ma, Junxia; Huang, Biao; Ding, Feng

doi:10.1049/iet-cta.2018.6236

Cited by 6 publications

(2 citation statements)

References 37 publications

(40 reference statements)

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“…A number of approaches have been developed for parametric uncertainty analysis [20][21][22][23], among which the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis in recent years [24,25]. The probability inversion method based on Bayesian theory sets the unknown parameters as random variables, and the probability density distribution function is used to describe the uncertainty of the results.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm

Zhang

Tian

Zhou

et al. 2021

IET Renewable Power Gen

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Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the differential evolution adaptive Metropolis algorithm (DREAM) based on the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis, but its application to the uncertainty analysis of PTGS has been rare. This study systematically evaluates the applicability of DREAM in the parameter identification and uncertainty quantification of PTGS. A real PSU in China has been employed as a case study for numerical experiments. Four groups of control experiments with different proportions of observation noises and different prior search spaces have been constructed in this study. It can be concluded from this study that: (a) accurate point identification results and effective uncertainty quantification can be obtained simultaneously using DREAM, (b) a lower proportion of observation noises can enhance the efficiency and effectiveness of the DREAM algorithm when applying to PTGS simulation. The DREAM method can narrow the prior search space of PTGS parameters effectively and thus help the hydropower engineers to make decisions.

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Section: Introductionmentioning

confidence: 99%

Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm

Zhang

Tian

Zhou

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Set membership estimation is a method to estimate the states or parameters of a system with unknown but bounded noise. It does not need the noise term satisfy a known probability distribution, which is different from the common used least‐squares algorithm [14], gradient searching algorithm [15], and Bayesian algorithm [16]. Its purpose is to obtain a set containing the real states or parameters of the system based on the system model, measurement data, and noise boundary that are jointly referred to as the feasible set of states or parameters.…”

Section: Introductionmentioning

confidence: 99%

Directional expansion‐based fault diagnosis algorithm using orthotopic and ellipsoidal filtering

Liu

Wang

Yan

et al. 2020

IET Control Theory & Appl

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Accurate and fast parameter identification of conditionally Gaussian Markov jump linear system with input control

Zhang

2022

Automatica

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Parameter estimation of Markov‐switching Hammerstein systems using the variational Bayesian approach

Cited by 6 publications

References 37 publications

Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm

Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm

Directional expansion‐based fault diagnosis algorithm using orthotopic and ellipsoidal filtering

Accurate and fast parameter identification of conditionally Gaussian Markov jump linear system with input control

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