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Multiway kernel independent component analysis based on feature samples for batch process monitoring

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: Author's personal copy Multiway kernel independent component analysis based on feature samples for batch process monitoring$ Xuemin Tian a, Xiaoling Zhang a, Xiaogang Deng a, Sheng Chen b,� a College of Information and Control Engineering, China University of Petroleum (Hua Dong), Donying, Shandong 257061, China b School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK a r t i c l e i n f o Article history: Received 7 May 2008 Received in revised form 21 August 2008 Accepted 18 September 2008 Communicated by T. Heskes Available online 9 October 2008 Keywords: Batch process Nonlinearity Kernel independent component analysis Feature samples a b s t r a c t Most batch processes generally exhibit the characteristics of nonlinear variation. In this paper, a nonlinear monitoring technique is proposed using a multiway kernel independent component analysis based on feature samples (FS-MKICA). This approach first unfolds the three-way dataset of a batch process into the two-way one and then chooses representative feature samples from the large two-way input training dataset. The nonlinear feature space abstracted from the unfolded two-way data space is then transformed into a high-d

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