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A Yarn Nep Prediction Method Combining Grey Correlation and Nearest Neighbour

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fenglong Wu & Chunxue Wei & Baowei Zhang, 2022. "A Yarn Nep Prediction Method Combining Grey Correlation and Nearest Neighbour," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-13, December.
    • Handle: RePEc:wsi:jikmxx:v:21:y:2022:i:04:n:s0219649222500526
    DOI: 10.1142/S0219649222500526
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