Prediction of Mechanical Properties of Cotton Fibers by a BP Neural Network Model Optimized by Genetic Algorithm

Abstract

In this experiment, a general purpose BP neural network (BP) based on genetic algorithm (GA) was developed for predicting fiber properties. The experiment is based on the breaking force of cotton fibers, and the controlled variable method is used for sampling test to collect 878 datasets containing four eigenvalues. The first 850 items of this dataset were then utilized to train the designed BP, and the remaining 28 items were evaluated for error. Next, the model is parameterized using a genetic algorithm to reduce the overall network size, thus optimizing the fit. Finally, the improved model was evaluated using the same dataset. The results were obtained: the MAPE was reduced from 10.94% to 3.7869%, the MAE was reduced from 0.39586 to 0.14584, and the MSE was reduced from 0.32161 to 0.05201. The results show that this GA-BP has better results for nonlinear fitting, and it can make a better correspondence to the outliers in the dataset, and also produces a smaller error in the fitting results, the Overall, the method is effective.

Keywords

• Neural networks
• material properties
• fitting and prediction
• 神经网络
• 材料性能
• 拟合和预测