RVM Soft Sensing Model Based on Optimized Combined Kernel Function

Authors

Yanan Zhang, Key Laboratory of Advanced Control of Light Industry Process in the Ministry of Education, Jiangnan University, Wuxi 214122, China;
Huizhong Yang, Key Laboratory of Advanced Control of Light Industry Process in the Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: An RVM spft sensingmodeling method based onthe optimizedcombined kernel functionis proposed.In order to simultaneously get better prediction and sparsity, a fitness function synthesizing regression accuracy and sparsity is created while constructing a combined kernel functionfor RVM.The genetic algorithm is used to optimize the weights and kernel parametersof the RVMcombined kernel.The proposed method is used totomodela cleavage-recovery unit in the production process of Bisphenol-A.The results show that it can guarantee better sparsity andregression accuracy than the general SVM combinedkernel model andGA-RVM single kernel model.

Recommended Citation

Zhang, Yanan and Yang, Huizhong (2019) "RVM Soft Sensing Model Based on Optimized Combined Kernel Function," Journal of System Simulation: Vol. 30: Iss. 1, Article 35.
DOI: 10.16182/j.issn1004731x.joss.201801035
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss1/35

First Page

272

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201801035

Last Page

277

CLC

TP274

Recommended Citation

Zhang Yanan, Yang Huizhong. RVM Soft Sensing Model Based on Optimized Combined Kernel Function[J]. Journal of System Simulation, 2018, 30(1): 272-277.

DOI

10.16182/j.issn1004731x.joss.201801035