Prediction of cotton yarn properties using support vector machine |
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Authors: | Anindya Ghosh Pritam Chatterjee |
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Institution: | 1.Government College of Engineering and Textile Technology,West Bengal,India |
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Abstract: | This paper presents a support vector machine (SVM) regression approach to forecast the properties of cotton yarns produced
on ring and rotor spinning technologies from the fibre properties measured by HVI and AFIS. Prediction performance of SVM
models have been compared against those of the artificial neural network (ANN) models. A k-fold cross validation technique is applied to assess the expected generalization accuracies of both SVM and ANN models. The
investigation indicates that the yarn properties can be predicted with a very high degree of accuracy using SVM models and
the prediction performance of SVM models are better than that of ANN models. |
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