Browsing by Author "Tokat, Sezai"
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Publication Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models(Springer, 2007-02) Tokat, Sezai; Çeven, Erhan Kenan; Özdemir, Özcan; Uludağ Üniversitesi/Mühendislik Fakültesi/Tekstil Mühendisliği Bölümü; 0000-0003-3283-4117; 0000-0003-2494-6485; AAG-4653-2019; B-1488-2019; 6504089018; 8577587200The 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.Item Statistical and computational intelligence tools for the analyses of warp tension in different back-rest oscillations(Elsevier Science, 2007-12-01) Turhan, Yıldıray; Tokat, Sezai; Eren, Recep; Uludağ Üniversitesi/Mühendislik Fakültesi/Tekstil Mühendisliği Bölümü.; 8649952300In this paper, experimental, computational intelligence based and statistical investigations of warp tensions in different back-rest oscillations are presented. Firstly, in the experimental stage, springs having different stiffnesses are used to obtain different back-rest oscillations. For each spring, fabrics are woven in different weft densities and the warp tensions are measured and saved during weaving process. Secondly, in the statistical investigation, the experimental data are analyzed by using linear multiple and quadratic multiple-regression models. Later, in the computational intelligence based investigation, the data obtained from the experimental study are analyzed by using artificial neural networks that are universal approximators which provide a massively parallel processing and decentralized computing. Specialty, radial basis function neural network structure is chosen. In this structure, cross-validation technique is used in order to determine the number of radial basis functions. Finally, the results of regression analysis, the computational intelligence based analysis and experimental measurements are compared by using the coefficient of determination. From the results, it is shown that the computational intelligence based analysis indicates a better agreement with the experimental measurement than the statistical analysis.