Performance of Error Back Propagation vis-á-vis Radial Basis Function Neural Network: Part I: Prediction of Properties for Design Engineering of Woven Suiting Fabrics

Abstract

In this paper, an artificial neural network approach is used to predict the low-stress mechanical, dimensional, and tensile properties of woven suiting fabrics. Radial basis function neural network prediction is studied along with error back propagation based neural networks. Radial basis function neural networks are found to have better predictability and are faster to train and easier to design than back propagation neural networks. The prediction of fabric properties by the neural networks due to changes in fabric constructional parameters is in good agreement with generally accepted trends for fibre, yarn and fabric structure property relationships.