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...
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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 algo-rithm 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.(c) 2022 Elsevier Inc. All rights reserved.
This article introduces a novel fault classification method based on the mixture robust probabilistic linear discriminant analysis (MRPLDA). Unlike conventional probabilistic models like probabilistic principal compon...
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This article introduces a novel fault classification method based on the mixture robust probabilistic linear discriminant analysis (MRPLDA). Unlike conventional probabilistic models like probabilistic principal component analysis (PPCA), probabilistic linear discriminant analysis (PLDA) introduces two sets of latent variables to represent the within-class and between-class information, resulting in an enhanced classification capability. In order to deal with outliers and non-Gaussian distributed variables commonly encountered in industrial processes, a mixture of robust PLDA model is considered by imposing the Student's t-priors on the noise and hidden variables of the PLDA model. Based on the model, a variational Bayesian expectation-maximization algorithm is developed for parameter estimation. In order to determine the state/class of a test sample, this article proposes a new state inference method by considering the joint probability between the test and training samples. The state inference method consists of a probability approximation, an evidence inference, and a voting based decision stage. The performance of the proposed fault classification method is illustrated by a numerical example and an application study to the Tennessee Eastman (TE) process. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
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