摘要
针对飞灰含碳量测量困难的问题,提出了基于粒子群算法优化BP神经网络的飞灰含碳量测量方法。以飞灰含碳量影响因素为模型的输入,飞灰含碳量为模型的输出,建立飞灰含碳量预测模型,并将预测结果和传统BP神经网络预测结果相比较。实验结果表明,该测量方法具有较高的预测精度。
In order to solve the problem of prediction of carbon content in fly ash, prediction method of carbon content in fly ash based on PSO-BP neural network was proposed. Taking the influencing factors of carbon content in fly ash as the input of the model and the carbon content in fly ash as the output of the model, the prediction model of carbon content in fly ash was established, and the prediction results were compared with the traditional BP neural network. The experimental results show that the method has high prediction accuracy.
作者
李力
陆金桂
LI Li;LU Jinggui(School of Mechanical and Dynamic Engineering,Nanjing Tech University,Nanjing 211816,China)
出处
《机械与电子》
2019年第4期68-71,76,共5页
Machinery & Electronics
关键词
粒子群算法
神经网络
飞灰含碳量
预测模型
particle swarm optimization
neural network
carbon content in fly ash
prediction model
