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Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models

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Abstract

The abrasion resistance of chenille yarn is crucially important in particular because the effect sought is always that of the velvety feel of the pile. Thus, various methods have been developed to predict chenille yarn and fabric abrasion properties. Statistical models yielded reasonably good abrasion resistance predictions. However, there is a lack of study that encompasses the scope for predicting the chenille yarn abrasion resistance with artificial neural network (ANN) models. This paper presents an intelligent modeling methodology based on ANNs for predicting the abrasion resistance of chenille yarns and fabrics. Constituent chenille yarn parameters like yarn count, pile length, twist level and pile yarn material type are used as inputs to the model. The intelligent method is based on a special kind of ANN, which uses radial basis functions as activation functions. The predictive power of the ANN model is compared with different statistical models. It is shown that the intelligent model improves prediction performance with respect to statistical models.

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Abbreviations

a k :

Coefficient of the kth independent variable

n :

Number of independent variables

b :

Bias term of multiple-regression models

b i :

Bias term of the ith hidden neuron

y 1 :

Fabric abrasion

y 2 :

Yarn abrasion

x 1 :

Yarn count

x 2 :

Pile length

x 3 :

Twist level

x 4 :

Pile yarn material type

c ik :

Coefficient of the interaction term x ik

α:

Column data vector

\( \bar{\varvec{\upalpha}} \) :

Normalized counterpart of vector α

m :

Number of columns of a data vector α

ones(m,1):

m×1 column vector with all elements as one

max(α):

Maximum element of vector α

min(α):

Minimum element of vector α

\( \bar{\varvec{\upalpha}}\) :

Normalized counterpart of data vector α

d i :

Input vector of the radial basis function R i

v i :

Center of the radial basis function R i

σ i :

Width of the radial basis function R i

w 2 ji :

Weight of the ith hidden unit for the jth output node

w 1 ji :

Weight of the ith input x i for the jth hidden unit

μ:

Learning rate

J :

Cost function

ΔJ :

Change in cost function

λ:

Adjusted parameter

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Authors and Affiliations

  1. Department of Textile Engineering, Uludag University Gorukle, Bursa, 16059, Turkey

    Erhan Kenan Çeven & Özcan Özdemir

  2. Department of Computer Engineering, Pamukkale University, Bilgisayar Muhendisligi Bolumu, Morfoloji Binası, Kinikli, Denizli, 20017, Turkey

    Sezai Tokat

Authors
  1. Erhan Kenan Çeven
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  2. Sezai Tokat
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  3. Özcan Özdemir
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Correspondence to Sezai Tokat.

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Çeven, E.K., Tokat, S. & Özdemir, Ö. Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models. Neural Comput & Applic 16, 139–145 (2007). https://doi.org/10.1007/s00521-006-0048-8

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  • DOI: https://doi.org/10.1007/s00521-006-0048-8

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