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

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 (term...

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Vydané v:	Information sciences Ročník 607; s. 493 - 505
Hlavní autori:	Yan, Aijun, Guo, Jingcheng, Wang, Dianhui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.08.2022
Predmet:	Expectation–maximization algorithm Robust data modeling Stochastic configuration networks Student’s-t mixture distribution Stochastic configuration networks Robust data modeling Expectation–maximization algorithm Student’s-t mixture distribution
ISSN:	0020-0255, 1872-6291
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