Journal of System Simulation
Abstract
Abstract: Industrial process information is highly nonlinear and dynamic, with long-term dependencies between data, making it difficult to adequately extract time-series features. To address this issue, an improved Transformer-based soft sensor model in a dual-stream framework was proposed. The data were segmented and expanded. The features were extracted in parallel using a dual-stream structure combining a convolutional neural network with a self-attention mechanism and the improved Transformer model. The dual-stream features were fused for soft sensor regression. Residual connections were further introduced to accelerate the convergence speed of the model, and an orthogonal random features-based improved multi-head attention mechanism was adopted to solve the vanishing gradient and non-parallelization issues faced by traditional recurrent neural networks. Numerical simulations have verified the effectiveness and superiority of the proposed method.
Recommended Citation
Gu, Hao; Wang, Jiayu; and Xiong, Weili
(2025)
"Soft Sensor Modeling Based on Improved Transformer in Dual-stream Framework,"
Journal of System Simulation: Vol. 37:
Iss.
10, Article 13.
DOI: 10.16182/j.issn1004731x.joss.24-0490
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss10/13
First Page
2594
Last Page
2604
CLC
TP274
Recommended Citation
Gu Hao, Wang Jiayu, Xiong Weili. Soft Sensor Modeling Based on Improved Transformer in Dualstream Framework[J]. Journal of System Simulation, 2025, 37(10): 2594-2604.
DOI
10.16182/j.issn1004731x.joss.24-0490
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