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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Bibliographic Details
Published in:Information sciences Vol. 607; pp. 493 - 505
Main Authors: Yan, Aijun, Guo, Jingcheng, Wang, Dianhui
Format: Journal Article
Language:English
Published: Elsevier Inc 01.08.2022
Subjects:
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 Access:Get full text
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Summary: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.05.105