Estimating a Product Quality by Support Vector Machines Method

Although many works have been done to construct prediction models on yarn processing quality, the relation between spinning variables and yarn properties has not been established conclusively so far. Support vector machines (SVMs), based on statistical learning theory, are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability. This study briefly introduces the SVM regression algorithms, and presents the SVM based system architecture for predicting yarn properties. Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method. Experimental results have been compared with those of ANN models. The investigation indicates that in the small data sets and real-life production, SVM models are capable of remaining the stability of predictive accuracy, and more suitable for noisy and dynamic spinning process.