MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions

Liu Yumin () and Zhou Haofei ()
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Liu Yumin: Business School, Zhengzhou University, Zhengzhou450001, China
Zhou Haofei: Business School, Zhengzhou University, Zhengzhou450001, China

Journal of Systems Science and Information, 2014, vol. 2, issue 5, 473-480

Abstract: Recognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. As the dynamic data from a quality abnormal process is linearly inseparable, the recognition efficiency of a support vector machine (SVM) model mainly depends on the selection of the kernel functions and the optimizing of their parameters. Based on the analysis of the quality abnormal patterns in a dynamic process, this paper presents a recognition framework of quality abnormal patterns by using a multi-SVM (MSVM). For the different quality abnormal patterns, the simulation results indicate that the recognition accuracies of the MSVM classifiers with the selected kernel functions are quite different. A MSVM recognition model for quality abnormal patterns in a dynamic process is proposed by the kernel functions being of high accuracies. It is shown that this MSVM model with suitable kernel functions can increase the recognition accuracy.

Keywords: dynamic process; quality abnormal pattern; kernel function; SVM; MSVM recognition model (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:2:y:2014:i:5:p:473-480:n:8

DOI: 10.1515/JSSI-2014-0473

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