Modeling Ultraviolet Protection Factor of Polyester-Cotton Blended Woven Fabrics Using Soft Computing Approaches

Abstract

Ultraviolet protection factor (UPF) of woven fabrics is modeled by using two soft computing approaches, namely adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Three fabric parameters: proportion of polyester in weft yarns, weft count, and pick density are used as input parameters for predicting fabric UPF. Two levels (low and high) of membership function for each of the input parameters are used to reduce the complexity of ANFIS. The eight linguistic fuzzy rules trained by ANFIS are able to explain the relationship between fabric parameters and UPF. A comparison between ANFIS and ANN models is also presented. Both the models predict the UPF of fabrics with very good prediction accuracy in the testing data sets.