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Algorithm Research & Explore
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127-130,152

Online monitoring strategy for batch process based on GLRGMM

Zhao Xiaoqiang1a,1b,2
Zhou Wenwei1a,2
1. a. College of Electrical & Information Engineering, b. National Experimental Teaching Center of Electrical & Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China

Abstract

Aiming at nonlinear and dynamic characteristics of batch process, this paper proposed a global-local regularization Gaussian mixture model(GLRGMM) algorithm. At first, this algorithm introduced neighborhood preserving embedding algorithm to extract the local manifold structure. It preserved global structure of nonlinear process by seeking a low-dimensional projection while preserved local manifold features to the maximum extent at the same time. Then it introduced the regularization term into GMM to online monitor and updated Gaussian model. It obtained the manifold structure of nonlinear data and solved the data dynamic characteristic problem simultaneously. Finally, it used the integration of global-local monitoring indicators to effectively achieve online monitoring. The results verified by the penicillin fermentation process show that the proposed algorithm has better online monitoring effect than DPCA and GLNPE algorithms.

Foundation Support

国家自然科学基金资助项目(61763029)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0441
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Algorithm Research & Explore
Pages: 127-130,152
Serial Number: 1001-3695(2020)01-026-0127-04

Publish History

[2020-01-05] Printed Article

Cite This Article

赵小强, 周文伟. 基于GLRGMM的间歇过程在线监控策略 [J]. 计算机应用研究, 2020, 37 (1): 127-130,152. (Zhao Xiaoqiang, Zhou Wenwei. Online monitoring strategy for batch process based on GLRGMM [J]. Application Research of Computers, 2020, 37 (1): 127-130,152. )

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