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Statistical Analysis of Nonlinear Processes Based on Penalty Factor

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  • Yingwei Zhang
  • Chuanfang Zhang
  • Wei Zhang

Abstract

A new process monitoring approach is proposed for handling the nonlinear monitoring problem in the electrofused magnesia furnace (EFMF). Compared to conventional method, the contributions are as follows: (1) a new kernel principal component analysis is proposed based on loss function in the feature space; (2) the model of kernel principal component analysis based on forgetting factor is updated; (3) a new iterative kernel principal component analysis algorithm is proposed based on penalty factor.

Suggested Citation

  • Yingwei Zhang & Chuanfang Zhang & Wei Zhang, 2014. "Statistical Analysis of Nonlinear Processes Based on Penalty Factor," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, September.
    • Handle: RePEc:hin:jnlmpe:945948
    DOI: 10.1155/2014/945948
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