Performance of soft sensors based on stochastic configuration networks with nonnegative garrote

• Published in: Neural computing & applications Vol. 34; no. 18; pp. 16061 - 16071
• Main Authors: Tian, Pengxin, Sun, Kai, Wang, Dianhui
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Approximation; Artificial Intelligence; Automation; Coal-fired power plants; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Configurations; Crude oil; Crude oil prices; Data Mining and Knowledge Discovery; Datasets; Distributed control systems; Feature selection; Image Processing and Computer Vision; Neural networks; Original Article; Probability and Statistics in Computer Science; Process controls; Sensors; Variables; Stochastic configuration networks; Soft sensor; Feature selection; Nonnegative garrote
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07254-w

In this study, stochastic configuration networks (SCNs) and nonnegative garrote (NNG) algorithm are employed to develop a soft-sensing technique that infers difficult-to-measure variables with easy-to-measure variables in industrial processes. The proposed method consists of two stages, that is, performing industrial data modeling with SCNs and applying NNG algorithm for shrinking input weights and removing some redundant input variables from the well-trained leaner model. Cross-validation and the Akaike information criterion are employed to determine the optimal shrinkage parameter for the NNG. A numerical example and real industrial data are used to validate the performance of the proposed algorithm. Several state-of-the-art feature selection schemes for neural networks are tested. Comparative results demonstrate that the proposed soft-sensor outperforms others in terms of the prediction accuracy.