Neural network models for fabric drape prediction

Neural networks are used to predict the drape coefficient (DC) and circularity (CTR) of many different kinds of fabrics. The neural network models used were the multilayer perceptron using backpropagation (BP) and the radial basis function (RBF) neural network. The BP method was found to be more effective than the RBF method but the RBF method was the fastest when it came to training. Comparisons of the two models as well as comparisons of the same models using different parameters are presented. It was also found that prediction for CIR was less accurate than for DC for both neural network architectures.