Skip to main content
SpringerLink
Log in

Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics

  • Published:
Fibers and Polymers Aims and scope Submit manuscript

Abstract

The aim of this study was to compare the response surface regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. The prediction models are based on the experimental data comprising yarn tenacity, knitting stitch length and fabric GSM as input variables and fabric bursting strength as output/response variable. The models quantitatively characterize the non-linear relationship and interactions between the input and output variables exhibiting very good prediction ability and accuracy, with ANFIS model being slightly better in performance than the regression model.

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

Similar content being viewed by others

References

  1. P. L. Chen, R. L. Barker, G. W. Smith, and B. Scruggs, Text. Res. J., 62, 200 (1992).

    Google Scholar 

  2. N. A. Tou, “An Investigation of Arcing in Two Structure Weft Knit Fabrics”, MSc. Thesis, North Carolina State University, Textile and Apparel Technology Management, 2005.

    Google Scholar 

  3. C. Rameshkumar, P. Anandkumar, P. Senthilnathan, R. Jeevitha, and N. Anbumani, Autex Res. J., 8, 100 (2008).

    Google Scholar 

  4. N. Emirhanova and Y. Kavusturan, Fibers Text. East. Eur., 16, 69 (2008).

    Google Scholar 

  5. G. Manonmani, C. Vigneswaran, and T. Ramachadran, J. Text. Apparel Technology and Management, 6, 1 (2010).

    Google Scholar 

  6. C. D. Kane, U. J. Patil, and P. Sudhakar, Text. Res. J., 77, 572 (2007).

    Article  CAS  Google Scholar 

  7. S. Mavruz and R. T. Ogulata, Fibers Text. East. Eur., 18, 78 (2010).

    Google Scholar 

  8. S. Ertugrul and N. Ucar, Text. Res. J., 70, 845 (2000).

    Article  CAS  Google Scholar 

  9. P. G. Unal, M. E. Ureyen, and D. M. Armakan, “ICAART 2010 — Proceedings of the International Conference on Agents and Artificial Intelligence”, Vol. 2010 — Proceedings of the International Conference on Agents and Artificial Intelligence”, Vol. 1 — Artificial Intelligence, Valencia, Spain, January 22–24, 2010.

  10. Z. A. Malik, M. H. Malik, T. Hussain, and A. Tanwari, Ind. J. Fibre Text. Res., 35, 310 (2010).

    CAS  Google Scholar 

  11. A. Majumdar, P. K. Majumdar, and B. Sarkar, J. Text. Inst., 96, 55 (2005).

    Article  Google Scholar 

  12. N. Ucar and S. Ertugrul, Text. Res. J., 72, 361 (2002).

    Article  CAS  Google Scholar 

  13. D. Nurwaha and X. H. Wang, Fiber. Polym., 9, 782 (2008).

    Article  Google Scholar 

  14. J. Ju and H. Ryu, Fiber. Polym., 7, 203 (2006).

    Article  Google Scholar 

  15. A. Majumdar, M. Ciocoiu, and M. Balga, Fiber. Polym., 9, 210 (2008).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering & Technology, National Textile University Faisalabad, Faisalabad, Pakistan

    Hafsa Jamshaid, Tanveer Hussain & Zulfiqar Ali Malik

Authors
  1. Hafsa Jamshaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tanveer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zulfiqar Ali Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tanveer Hussain.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jamshaid, H., Hussain, T. & Malik, Z.A. Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. Fibers Polym 14, 1203–1207 (2013). https://doi.org/10.1007/s12221-013-1203-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12221-013-1203-3

Keywords

Search

Navigation