人工神经网络预测煤炭成浆浓度的研究

考虑煤炭的多种理化特性建立了成浆浓度的神经网络预测模型，对其数据预处理方法、学习率和中间层节点数等进行了深入讨论。水分、挥发分、分析基碳、灰分和氧等五个因子对于煤炭成浆性的预测起到主导作用。五因子、七因子和八因子神经网络模型对煤炭成浆浓度的预测误差分别为：0.53%、0.50%和0.74%，而现有回归分析方程的误差为1.15%，故神经网络模型比回归分析方程有更好的预测能力，尤以七因子模型最佳。

Prediction of coal slurry concentration based on artificial neural networks

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.