Journal of System Simulation
Abstract
Abstract: To address the challenges posed by nonlinearity and the coupling of multiple features in complex industrial processes, resulting in increased model complexity and decreased performance, a soft sensor modeling method based on adaptive sparse broad learning system is proposed. Building upon the lateral enhancement transmission of features, the trace least absolute shrinkage and selection operator (LASSO) is further used to optimize the feature weights of the network, adaptively adjusting the penalty intensity based on the correlation between different variables to enhance the feature extraction capabilities of the model. The Dropout mechanism is introduced in the enhanced part, and the output weights are utilized by LASSO to sparsely optimize the model as a whole, eliminating excess nodes, and reducing redundant data in the calculation process. Experimental results show that the proposed method can effectively simplify the model structure and improve its prediction performance.
Recommended Citation
Du, Kangping; Sui, Lin; and Xiong, Weili
(2025)
"Soft Sensor Modeling Based on Adaptive Sparse Broad Learning System⋅,"
Journal of System Simulation: Vol. 37:
Iss.
6, Article 10.
DOI: 10.16182/j.issn1004731x.joss.24-0096
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss6/10
First Page
1449
Last Page
1461
CLC
TP274
Recommended Citation
Du Kangping, Sui Lin, Xiong Weili. Soft Sensor Modeling Based on Adaptive Sparse Broad Learning System[J]. Journal of System Simulation, 2025, 37(6): 1449-1461.
DOI
10.16182/j.issn1004731x.joss.24-0096
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