A Yarn Nep Prediction Method Combining Grey Correlation and Nearest Neighbour

Fenglong Wu, Chunxue Wei and Baowei Zhang
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Fenglong Wu: Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, P. R. China
Chunxue Wei: ��Henan Light Industry Vocational College, Zhengzhou, Henan 450002, P. R. China
Baowei Zhang: Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, P. R. China

Journal of Information & Knowledge Management (JIKM), 2022, vol. 21, issue 04, 1-13

Abstract: In recent years, there exist few difficulties for textile industries to predict the yarn nep index for small data and data with mutation. To fill this gap, a yarn nep prediction method combining grey correlation analysis and nearest-neighbour prediction method is proposed. In this paper, 26 indicators such as the raw cotton quality indicators and key process parameters are used as the input of the prediction model for yarn nep. The experimental results show that the relative error of the new method is lower than 10%, while the relative error of the individual data predicted by the traditional three-layer BP neural network is very large. Compared with the BP neural network, the average relative error and root-mean-square error of our proposed method are smaller, while the data are stable and the volatility is small. The prediction performance meets the user’s requirements. The effectiveness of the proposed model is proved.

Keywords: Grey correlation; nearest neighbour; BP neural network; yarn nep index (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219649222500526

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