Skip to main content
SpringerLink
Log in

The prediction model of worsted yarn quality based on CNN–GRNN neural network

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

It is key indexes of worsted yarn quality such as worsted yarn strength index, etc., and it can well control worsted yarn quality by predicting yarn strength index, etc. Generally, it is generally used to predict yarn strength indexes such as multiple linear regression (MLR) algorithm, support vector machine (SVM) and backpropagation neural network (BPNN). This paper proposes a new neural network; it combines convolutional neural network (CNN) with general regression neural network (GRNN), which is written as the CNN–GRNN. It used 1900 sets of data to train CNN–GRNN, SVM and BPNN. It tested CNN–GRNN, MLR, SVM and BPNN with 10 sets of data. The CNN–GRNN neural network is the best accuracy among these four algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Kim JS, Sim JY, Kim CS (2014) Multiscale saliency detection using random walk with restart. IEEE Trans Circuits Syst Video Technol 24(2):198–210

    Article  Google Scholar 

  2. Cheng MM, Zhang GG, Mita NJ et al (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582

    Article  Google Scholar 

  3. Liang Z, Wang M, Zhou X et al (2014) Salient object detection based on regions. Multimed Tools Appl 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 Trans Pattern Anal Mach Intell 38(4):717–729

    Article  Google Scholar 

  5. Wang B, Pan F, Hu KM et al (2012) Manifold-ranking based retrieval using k-regular nearest neighbour graph. Pattern Recogn 45(4):1569–1577

    Article  Google Scholar 

  6. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge, pp 82–92

    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. IEEE, Piscataway, pp 1026–1034

  8. Hinton GE, Srivastava N, Krizhevsky A et al. Improving neural networks by preventing co-adaption of feature detectors [R/OL].2015-10-26. 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 unrecognizable images. In: Proceedings of the 2015 ieee conference on computer vision and pattern recognition. IEEE Computer Society, Washington, pp 427–436

  10. Cheng MM, Zhang GX, Mitra NJ et al (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 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: CVPR ‘13: Proceedings of the 2013 IEEE conference on computer vision and pattern recognition. IEEE Computer Society, Washington, pp 3166–3173

  12. Zhou D, Weston J, Gretton A et al (2015) Ranking on data manifolds. www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf2334.pdf. Accessed 08 Nov 2015

  13. Achanta R, Shaji A, Smith K et al (2015) SLIC superpixels. http://islab.ulsan.ac.kr/files/announce-ment/531/SLIC_Superpixels.pdf. Accessed 11 Nov 2015

  14. Goodfellow IJ, Warde-Farley D, Mirza M et al (2016) Maxout network. http://www-etud.iro.umontrealca/goodfeli/maxout.pdf. Accessed 12 Jan 2016

  15. Lin M, Chen Q, Yan S (2016) Network in network. http://arxiv.org/pdf/4400v3.pdf. Accessed 12 Jan 2016

  16. Williamson DS, Wang YX, Wang DL (2015) Estimating nonnegative matrix model activations with deep neural networks to increase perceptual speech quality. J Acoust Soc Am 138(3):1399–1407

    Article  Google Scholar 

  17. Ouyang WL, Wang XG (2013) Joint deep learning for pedestrian detection. In: Proceedings of the IEEE international conference on computer vision. Sydney, Australia, pp 2056–2063

  18. Ouyang WL, Chu X, Wang XG (2014) Multi-source deep learning for human pose estimation. In: Proceedings of the ieee international conference on computer vision and pattern recognition. Columbus, pp 2337–2344

  19. Sun Y, Wang XG, Tang XO (2013) Hybrid deep learning for face verification. In: Proceedings of the IEEE international conference on computer vision. Sydney, Australia, pp 1489–1496

  20. Wan J, Wang DY, Hoi SCH et al (2014) Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the ACM international conference on multimedia. Orlando, pp 157–166

  21. Dong C, Loy CC, He KM, Tang XO (2014) Learning a deep convolutional network for image super-resolution. In: Proceedings of the European conference on computer vision. Zurich, Switzerland, pp 184–199

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Zhenlong Hu, Qiang Zhao & Jun Wang

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

    Zhenlong Hu

Authors
  1. Zhenlong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Zhao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Z., Zhao, Q. & Wang, J. The prediction model of worsted yarn quality based on CNN–GRNN neural network. Neural Comput & Applic 31, 4551–4562 (2019). https://doi.org/10.1007/s00521-018-3723-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3723-7

Keywords

Search

Navigation