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

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, per...

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Bibliographic Details
Published in:Neural computing & applications 2022-09, Vol.34 (18), p.16061-16071
Main Authors: Tian, Pengxin, Sun, Kai, Wang, Dianhui
Format: Article
Language:English
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
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07254-w