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

Author

Listed:
  • Liu Yumin

    (Business School, Zhengzhou University, Zhengzhou450001, China)

  • Zhou Haofei

    (Business School, Zhengzhou University, Zhengzhou450001, China)

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.

Suggested Citation

  • Liu Yumin & Zhou Haofei, 2014. "MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 473-480, October.
    • Handle: RePEc:bpj:jossai:v:2:y:2014:i:5:p:473-480:n:8
    DOI: 10.1515/JSSI-2014-0473
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    References listed on IDEAS

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    1. Jianjun Shi & Shiyu Zhou, 2009. "Quality control and improvement for multistage systems: A survey," IISE Transactions, Taylor & Francis Journals, vol. 41(9), pages 744-753.
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