Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics

Abstract

The aim of this study was to compare the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for predicting the wrinkle recovery of polyester/cotton woven fabrics. The prediction models were developed using experimental data-set of 115 fabric samples of different constructions. Warp and weft yarn linear densities, ends/25 mm and picks/25 mm, were used as input/predictor variables, and warp and weft crease recovery angles (CRA) as output/response variables. It was found that the prediction accuracy of the ANN models was slightly better as compared with that of ANFIS models developed in this study. However, the ANFIS models could characterize the relationships between the input and output variables through surface plots, which the ANN models could not. The developed models may be used to optimize the fabric construction parameters for maximizing the wrinkle recovery of polyester/cotton woven fabrics.