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Modelling of ring yarn unevenness by soft computing approach

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Abstract

This paper demonstrates the application of two soft computing approaches namely artificial neural network (ANN) and neural-fuzzy system to forecast the unevenness of ring spun yarns. The cotton fiber properties measured by advanced fiber information system (AFIS) and yarn count have been used as inputs. The prediction accuracy of the ANN and neural-fuzzy models was compared with that of linear regression model. It was found that the prediction performance was very good for all the three models although ANN and neural-fuzzy models seem to have some edge over the linear regression model. The linguistic rules developed by the neural-fuzzy system unearth the role of input variables on the yarn unevenness.

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Author notes

  1. Abhijit Majumdar

    Present address: Department of Textile Technology, Indian Institute of Technology, New Delhi, 110016, India

Authors and Affiliations

  1. Government College of Engineering and Textile Technology, Berhampore, 742101, India

    Abhijit Majumdar

  2. Faculty of Textile and Leather Engineering, Technical University of Iasi, Iasi, 70050, Romania

    Mihai Ciocoiu & Mirela Blaga

Authors
  1. Abhijit Majumdar
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  2. Mihai Ciocoiu
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  3. Mirela Blaga
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Correspondence to Abhijit Majumdar.

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Majumdar, A., Ciocoiu, M. & Blaga, M. Modelling of ring yarn unevenness by soft computing approach. Fibers Polym 9, 210–216 (2008). https://doi.org/10.1007/s12221-008-0034-0

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  • DOI: https://doi.org/10.1007/s12221-008-0034-0

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