人工神经网络预测煤炭成浆浓度的研究

Based on experimental data of coal slurry, three BP neural network models with 8, 7 and 5 input factors, were set up for predicting the slurry concentration. Three BP neural networks’ algorithm was Levenberg-Marquardt algorithm, and their learning rate was 0.01. The hidden neurons number was settled by practical training effect of the networks. The hidden neurons number of BP model with 8, 7 and 5 input factors is 27, 30 and 24, respectively. Two data treated method were tested by seven input factors network model, which proves that the first method is the better one. The mean absolute error of the neural network models with 5, 7 and 8 factors is 0.53%, 0.50% and 0.74%, respectively, while that of the existed regression model is 1.15%. This indicates that the neural network models, especially the 7 factors’ model, are effective in predicting the slurry. The HGI input neuron in eight input factors’ model affects the prediction result because of its interference to other input factors. The effect of H and N in coal on the slurry is slight.