ORIGINAL ARTICLE Erhan Kenan C¸ even Æ Sezai Tokat Æ O ¨ zcan O ¨ zdemir Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models Received: 15 June 2005 / Accepted: 3 March 2006 / Published online: 1 April 2006 Ó Springer-Verlag London Limited 2006 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. Keywords Prediction Æ Artificial neural networks Æ Radial basis functions Æ Chenille yarn Æ Abrasion resistance Abbreviations a k : Coefficient of the kth independent variable Æ n: Number of independent variables Æ b: Bias term of multiple-regression models Æ b i : Bias term of the ith hidden neuron Æ y 1 : Fabric abrasion Æ y 2 : Yarn abrasion Æ x 1 : Yarn count Æ x 2 : Pile length Æ x 3 : Twist level Æ x 4 : Pile yarn material type Æ c ik : Coefficient of the interaction term x ik Æ a: Column data vector Æ  a: Normalized counterpart of vector a Æ m: Number of columns of a data vector a Æ ones(m,1): m·1 column vector with all elements as one Æ max(a): Maximum element of vector a Æ min(a): Minimum element of vector a Æ  a: Normalized counterpart of data vector a Æ d i : Input vector of the radial basis function R i Æ v i : Center of the radial basis function R i Æ r i : Width of the radial basis function R i Æ w ji 2 : Weight of the ith hidden unit for the jth output node Æ w ji 1 : Weight of the ith input x i for the jth hidden unit Æ l: Learning rate Æ J: Cost function Æ DJ: Change in cost function Æ k: Adjusted parameter 1 Introduction Chenille yarn is composed of two highly twisted core yarns and short lengths of a pile (effect) yarn which project out from the core to give a hairy effect [15]. The short lengths are called the pile, and the highly twisted yarns are called the core. The result is a yarn with a velvet-like or pile surface [7, 17]. Chenille yarns are used to produce special knitted and woven fabrics with high added value. A disadvan- tage of chenille yarn is its distinct weakness. Thus, it does not have a good inherent abrasion resistance. Any removal of the effect yarn forming the pile, either during further processing or during the eventual end-use, will expose the ground yarns, which in turn will result in a bare appearance [10]. Abrasion resistance, like other yarn properties, is chiefly influenced by fiber properties, yarn twist and yarn count [2, 3, 5, 8]. The literature survey shows that there are few studies about the fun- damental parameters that characterize chenille yarns. Statistical models (analysis of variance) yielded reason- ably good abrasion resistance predictions [17]. It would be helpful if a prediction model could forecast chenille yarn and fabric abrasion resistance accurately. E. K. C¸ even Æ O ¨ .O ¨ zdemir Department of Textile Engineering, Uludag University Gorukle, Bursa 16059, Turkey E-mail: rceven@uludag.edu.tr E-mail: ozdemir@uludag.edu.tr S. Tokat (&) Department of Computer Engineering, Pamukkale University, Bilgisayar Muhendisligi Bolumu, Morfoloji Binası, Kinikli, Denizli 20017, Turkey E-mail: stokat@pamukkale.edu.tr Tel.: +90-258-2134030 Fax: +90-258-2125538 Neural Comput & Applic (2007) 16: 139–145 DOI 10.1007/s00521-006-0048-8