Electronics Optics & Control, Volume. 26, Issue 6, 18(2019)

Influence of Similarity Measure Based Improved KS Algorithm on Near-Infrared Spectroscopy Analysis Model

GAO Yunfei1... FU Linyu1, QU Jun1, WANG Juxiang1, XING Zhina1 and WENG Xinhua2 |Show fewer author(s)
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  • 1[in Chinese]
  • 2[in Chinese]
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    The effective sample partition in the near-infrared spectroscopy model is studied.When classical Kennard Stone (KS) algorithm uses the distance metric to describe the difference between high-dimensional spectral data, the effect is unsatisfactory or even meaningless.To solve the problem, and considering the shortcomings of current similarity measurement methods, we constructed a new similarity measure function.The spectral features and property features were combined to calculate the difference between the samples.An improved KS algorithm was thus proposed to find the best expression of sample difference. The improved algorithm was analyzed from the aspects of effectiveness and the impact on the near-infrared spectroscopy model by comparing with other improved methods, and the rationality and superiority of the proposed algorithm were verified.

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    GAO Yunfei, FU Linyu, QU Jun, WANG Juxiang, XING Zhina, WENG Xinhua. Influence of Similarity Measure Based Improved KS Algorithm on Near-Infrared Spectroscopy Analysis Model[J]. Electronics Optics & Control, 2019, 26(6): 18

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    Paper Information

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    Received: Jul. 27, 2018

    Accepted: --

    Published Online: Jan. 5, 2021

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2019.06.004

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