Comparison of the new methodologies for predicting the CSP strength of rotor yarn

Abstract

This paper provides preliminary results on the relative performance of the adaptive neuro-fuzzy system inference (ANFIS) model versus linear multiple regression method, when applied to the use of cotton fiber properties to predict spun yarn strength obtained from open-end rotor spinning. Fiber properties and yarn count are used as inputs to train the two models and the output (dependent variable) would be the count-strength-product (CSP) of the yarn. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better average prediction successful in comparison with linear multiple regression model.