Prediction of mechanical properties of aging B.mori silk fabric based on grey neural network model

This paper presents a grey neural network model for the prediction of mechanical properties of aging B.mori silk fabric. In the experiment, we obtained outdoor natural aging breaking strength of B.mori silk fabric from 8 samples. Then, a grey neural network GNNM (1,1) model is proposed by the means of combining GM (1,1) model with BP artificial neural network to predict mechanical properties of B.mori silk fabric. At the same time, this paper analyzed and compared the GM (1,1) model and GNNM (1,1) model by using prediction error such as the relative percentage error (RPE) and the root mean square error (RMSE). The experimental results show that the RMSE of GNNM (1,1) model is 0.0284 well below 6.1786, which is the RMSE of GM (1,1) model. It indicates the GNNM (1,1) model were better than the normal grey GM (1,1) model, when taken the prediction error as evaluation parameter.