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Performance of neural network algorithms during the prediction of yarn breaking elongation

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Yarn breaking elongation is one of the most important yarn quality characteristics, since it affects the manufacture and usability of woven and knitted fabrics. One of the methods used to predict the breaking elongation of ring spun yarn is artificial neural network (NN). The design of an NN involves the choice of several parameters which include the network architecture, number of hidden layers, number of neurons in the hidden layers, training, learning and transfer functions. This paper endeavors to study the performance of NN as the design factors are varied during the prediction of cotton ring spun yarn breaking elongation. A study of the relative importance of the input parameters was also undertaken. The results indicated that there is a significant difference in the types of transfer and training functions used. Of the two transfer functions used, purelin performed far much better than logsig function. Among the five training functions, the best training functions in terms of performance was Levenberg-Marquardt. The study of the relative importance of input factors revealed that yarn twist, yarn count, fiber elongation, length, length uniformity and spindle speed, were the six most influential factors.

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  1. D. J. McCreight, R. W. Feil, J. H. Booterbaugh, and E. E. Backe, “Short Staple Yarn Manufacturing”, pp.458–462, Carolina Academic Press, Durham, North Carolina, 1997.

    Google Scholar 

  2. S. Doonmez and A. Marmarali, Text. Res. J., 74(12), 1049 (2004).

    Google Scholar 

  3. R. D. Anandjiwala and B. C. Goswami, Text. Res. J., 63(7), 392 (1993).

    CAS  Google Scholar 

  4. S. Kovacevic, K. Hajdarovic, and A. M. Grancaric, Text. Res. J., 70(7), 603 (2000).

    Google Scholar 

  5. S. D. Kretzschmar, A. T. Ozguney, G. Ozcelik, and A. Ozerdem, Text. Res. J., 77(4), 233 (2007).

    Article  CAS  Google Scholar 

  6. P. K. Majumdar and A. Majumdar, Text. Res. J., 74(7), 652 (2004).

    Article  CAS  Google Scholar 

  7. F. M. Ham and I. Kostanic, “Principles of Neurocomputing for Science & Engineering”, pp.132–135, 222–226, China Machine Press, Beijing, 2003.

    Google Scholar 

  8. M. T. Hagan, H. B. Demuth, and M. Beale, “Neural Network Design”, China Machine Press, Beijing, 2002.

    Google Scholar 

  9. S. M. Ishtiaque, R. S. Rengasamy, and A. Ghosh, Indian J. Fibre Text. Res., 29, 190 (2004).

    CAS  Google Scholar 

  10. E. Mustafa and U. H. Kadoglu, Text. Res. J., 76(5), 360 (2006).

    Article  CAS  Google Scholar 

  11. K. Douglas, Uster News Bulletin, 38, 23 (1991).

    Google Scholar 

  12. W. Zurek, I. Frydrych, and S. Zakrzewski, Text. Res. J., 57(8), 439 (1987).

    Google Scholar 

  13. L. A. Fiori, J. E. Sands, H. W. Little, and J. N. Grant, Text. Res. J., 26(7), 553 (1956).

    Google Scholar 

  14. W. P. Virgin and H. Wakeham, Text. Res. J., 26(3), 177 (1956).

    Article  Google Scholar 

  15. H. B. Demuth, M. Beale, and M. T. Hagan, “Neural Network Toolbox, For Use with MATLAB”, User’s Guide Version 4, The MathWorks Inc., Natick, 2005.

    Google Scholar 

  16. J. L. Elman, Cognitive Science, 14, 179 (1990).

    Article  Google Scholar 

  17. E. Mizutani and J. S. R. Jang in “In Neuro-Fuzzy and Soft Computing”, (J. S. R Jang, C. T. Sun, and E. Mizutani Eds.), pp.129–172, Prentice Hall, Upper Saddle River, 1997.

    Google Scholar 

  18. C. A. Lawrence, “Fundamentals of Spun Yarn Technology”, pp.359–406, CRC Press LLC, Florida, 2003.

    Google Scholar 

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Correspondence to Josphat Igadwa Mwasiagi.

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Mwasiagi, J.I., Huang, X. & Wang, X. Performance of neural network algorithms during the prediction of yarn breaking elongation. Fibers Polym 9, 80–86 (2008).

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