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The Prediction Model of Cotton Yarn Intensity Based on the CNN-BP Neural Network

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Abstract

Yarn strength index is a heavy index of yarn quality, Yarn quality can be well controlled by predicting yarn strength index. Generally, multiple non regression algorithms, support vector machines (SVD) and BP neural network algorithms are generally used to predict yarn strength. This paper presents an algorithm to connect the convolution neural network (CNN) with the BP neural network, which is written as the CNN-BP algorithm. We use 20 sets of data to train CNN-BP algorithm, regression, V-SVD algorithm, and BP neural network. We tested CNN-BP algorithm, regression, V-SVD algorithm, and BP neural network with 5 sets of data. The CNN-BP neural network algorithm is the best in these four algorithms.

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References

  1. Kim, J. S., Sim, J. Y., & Kim, C. S. (2014). Multiscale saliency detection using random walk with restart. IEEE Transactions on Circuits and Systems for Video Technology, 24(2), 198–210.

    Article  Google Scholar 

  2. Cheng, M. M., Zhang, G. X., Mita, N. J., et al. (2015). Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 569–582.

    Article  Google Scholar 

  3. Liang, Z., Wang, M., Zhou, X., et al. (2014). Salient object detection based on regions. Multimedia Tools and Applications, 68(3), 517–544.

    Article  Google Scholar 

  4. Shi, J., Yan, Q., Xu, L., et al. (2016). Hierarchical image saliency detection on extended CSSD. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(4), 717–729.

    Article  Google Scholar 

  5. Wang, B., Pan, F., Hu, K. M., et al. (2012). Manifold-ranking based retrieval using k-regular nearest neighbor graph. Pattern Recognition, 45(4), 1569–1577.

    Article  Google Scholar 

  6. Murphy, K. P. (2012). Machine learning: A probabilistic perspective (pp. 82–92). Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  7. He, K., Zhang, X., & Ren, S., et al. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proceedings of the 2015 IEEE international conference on computer vision (pp. 1026–1034). Piscataway, NJ: IEEE.

  8. Hinton, G. E., Srivastava, N., & Krizhevsky, A., et al. (2015). Improving neural networks by preventing co-adaption of feature detectors [R/OL]. Accessed on October 26, 2015, from http://arxiv.org/pdf/1207.0580v1.pdf.

  9. Nguyen, A.,Yosinski, J., & Clune, J., et al. (2015) Deep neural networks are easily fooled: High confidence predictions for unrecog-nizable images. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (pp. 427–436). Washington, DC: IEEE Computer Society.

  10. Cheng, M. M., Zhang, G. X., Mitra, N. J., et al. (2015). Global contrast based salient region detection. IEEE Transactions on Patter Analysis and Machine Intelligence, 37(3), 569–582.

    Article  Google Scholar 

  11. Yang C, Zhang L, Lu H, et al. (2013). Saliency detection via graph based manifold ranking. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ‘13 (pp. 3166–3173). Washington, DC: IEEE Computer Society.

  12. Zhou, D., Weston, J., & Gretton, A., et al. (2015). Ranking on data manifolds [EB/OL]. Accessed on November 08, 2015 from http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf2334.pdf.

  13. Achanta, R., Shaji, A., & Smith, K., et al. (2015). SLIC super pixels [EB/OL]. Accessed on December 11, 2015 from http://islab.ulsan.ac.kr/files/announce-ment/531/SLIC_Superpixels.pdf.

  14. Goodfellow, I. J., Warde-Farley, D., & Mirza, M., et al. (2016). Maxout network [EB/OL]. Accessed on January 12, 2016 from http://www-etud.iro.umontreal.ca/~goodfeli/maxout.pdf.

  15. Lin, M., Chen, Q., & Yan, S. (2016). Network in network [EB/OL]. Accessed on January 12, 2016 from http://arxiv.org/pdf/13124400v3.pdf.

  16. Murphy, K. P. (2012). Machine learning: A probabilistic perspective (pp. 82–92). Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  17. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

    Article  Google Scholar 

  18. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.

  19. Lee, H., Grosse, R., Ranganath, R., et al. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. ICML ’09. In Proceedings of the 26th Annual inter-national conference on machine learning. New York: ACM, (pp. 609–616).

  20. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of the 2005 IEEE Conference on computer vision and pattern recognition. Washington, IEEE Computer Society, (pp. 886–893).

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Authors and Affiliations

  1. College of Textiles, Donghua University, Shanghai, China

    Hu Zhenlong, Zhao Qiang & Wang Jun

  2. College of Network Communication, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, China

    Hu Zhenlong

Authors
  1. Hu Zhenlong
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  2. Zhao Qiang
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  3. Wang Jun
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Correspondence to Hu Zhenlong.

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Zhenlong, H., Qiang, Z. & Jun, W. The Prediction Model of Cotton Yarn Intensity Based on the CNN-BP Neural Network. Wireless Pers Commun 102, 1905–1916 (2018). https://doi.org/10.1007/s11277-018-5245-0

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  • DOI: https://doi.org/10.1007/s11277-018-5245-0

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