Journal of System Simulation
Abstract
Abstract: Due to the strong nonlinearity, multi-stage coupling, and the small number of labeled samples in complex industrial processes, it is difficult for traditional global soft sensor models to accurately describe the whole process. Therefore, a multi-model soft sensor modeling method under the helptraining strategy is proposed. This method uses a fuzzy C-means (FMC) clustering algorithm to mine similar samples in the sample set and build several sub-models. By introducing the help-training strategy, a collaborative training framework based on main and auxiliary learners is formed, and a confidence evaluation mechanism is designed to eliminate error samples and expand the modeling space of submodels. Then the fuzzy membership degree is used as the probability distribution function of D-S evidence theory to calculate the weight of the sub-model, and the output of the sub-model is fused to obtain the final model prediction result. Through the modeling and simulation of the actual data of the debutanizer industrial process, the results show that this model has good prediction performance.
Recommended Citation
He, Luosuyang and Xiong, Weili
(2024)
"Multi-model Soft Sensor Modeling under Help-training Strategy,"
Journal of System Simulation: Vol. 36:
Iss.
1, Article 19.
DOI: 10.16182/j.issn1004731x.joss.22-0976
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss1/19
First Page
249
Last Page
259
CLC
TP274
Recommended Citation
He Luosuyang, Xiong Weili. Multi-model Soft Sensor Modeling under Help-training Strategy[J]. Journal of System Simulation, 2024, 36(1): 249-259.
DOI
10.16182/j.issn1004731x.joss.22-0976
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