Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns

19-25The breaking elongation of rotor-spun yarns has been predicted by using linear regression, artificial neural network and neuro-fuzzy models. Cotton fibre properties measured by high volume instrument and yarn count have been used as inputs to the prediction models. Prediction accuracy is found to be better for artificial neural network and neuro-fuzzy models than that for regression modeI. The relative importance of yarn count and cotton fibre properties to rotor yarn elongation has also been studied. Yarn count and cotton fibre micronaire are found to be dominant input factors influencing the breaking elongation of rotor-spun yarns