Application of multiple linear regression and artificial neural network algorithms to predict the total hand value of summer knitted T-shirts

Abstract

A mathematical method, Weighted Euclidean Distance, has been applied for indirect determination of total hand value from the KES system parameters obtained for various summer knitted T-shirts. In this method, the weight of multivariable related to fabric hand has been determined from objective measurements without any resource to subjective evaluation. Artificial neural network with back propagation learning algorithm and multiple linear regression algorithm have been used to construct predictive models for the determination of total hand value of summer knitted T-shirts based on fabric mechanical properties measured on the KES system of each sample as input and total hand value predicted by mathematical model as desired output. The predictive power of optimized models is calculated and compared. The results reveal that the artificial neural network model is very effective for predicting the total hand value and has the better performance as compared to multiple linear regression model.