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research-article

Robust stochastic configuration networks for industrial data modelling with Student’s-t mixture distribution

Authors:

Dianhui WangAuthors Info & Claims

Volume 607, Issue C

Pages 493 - 505

https://doi.org/10.1016/j.ins.2022.05.105

Published: 01 August 2022 Publication History

Abstract

Data collected from industrial sites commonly contains outliers or noise that obey unknown distributions, making it challenging to establish an accurate data-driven model. Therefore, this paper proposes a novel robust stochastic configuration network based on a Student’s-t mixture distribution (termed as SM-RSC). Firstly, a stochastic configuration algorithm is employed to determine the number of hidden nodes, the input weights and biases. Secondly, the maximum a posteriori (MAP) estimate is used to evaluate the output weights of the SCN learner model under the assumption that outliers or noises obey the Student’s-t mixture distribution. Because the output weights cannot be solved directly due to the unknown hyper-parameters of the mixture distribution, we apply the well-known expectation–maximization (EM) algorithm for optimizing the hyper-parameters of the mixture distribution and update the output weights iteratively. The proposed algorithm is evaluated by a function approximation, four benchmark datasets, and a case study on industrial data modelling for a waste incineration process. The results show that SM-RSC performs favorably compared with other methods.

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Cited By

Li KQiao JWang D(2024)Fuzzy Stochastic Configuration Networks for Nonlinear System ModelingIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.331536832:3(948-957)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3315368
Meng LDing JLi XCao GLi YZhang D(2024)Novel shape control system of hot-rolled strip based on machine learning fused mechanism modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124789255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124789
Yan AWang RGuo JTang J(2024)A knowledge transfer online stochastic configuration network-based prediction model for furnace temperature in a municipal solid waste incineration processExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122733243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122733
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Robust stochastic configuration networks for industrial data modelling with Student’s-t mixture distribution

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 607, Issue C

Aug 2022

1637 pages

ISSN:0020-0255

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 August 2022

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Cited By

Li KQiao JWang D(2024)Fuzzy Stochastic Configuration Networks for Nonlinear System ModelingIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.331536832:3(948-957)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3315368
Meng LDing JLi XCao GLi YZhang D(2024)Novel shape control system of hot-rolled strip based on machine learning fused mechanism modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124789255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124789
Yan AWang RGuo JTang J(2024)A knowledge transfer online stochastic configuration network-based prediction model for furnace temperature in a municipal solid waste incineration processExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122733243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122733
Wang XMa JXiong W(2023)Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noiseInternational Journal of Adaptive Control and Signal Processing10.1002/acs.365737:10(2706-2724)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1002/acs.3657

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