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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Veröffentlicht in:	Information sciences Jg. 607; S. 493 - 505
Hauptverfasser:	Yan, Aijun, Guo, Jingcheng, Wang, Dianhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.08.2022
Schlagworte:	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
Online-Zugang:	Volltext
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