一种基于耦合算法的矿井风流温度预测新方法

A New Method of Mine Airflow Temperature Prediction Based on Coupling Algorithm

针对矿井风流温度预测工作的复杂性及各个影响因素的模糊的非线性关系,传统预测方法难以构建预测模型,导致预测精度低的特点,提出一种基于RBF神经网络的矿井风流温度预测方法;并利用粒子群算法对RBF神经网络参数进行寻优,利用煤矿历史数据对预测模型进行仿真研究。结果表明,提出的基于改进粒子群算法的RBF神经网络模型(MPSO-RBF)具有收敛速度快,预测精度高的特点,为矿井风流温度预测领域提供理论支撑。

Aiming at the complexity of airflow temperature prediction in the mine and the nonlinear relationship of the various factors, traditional forecasting method is difficult to construct the forecast model, which results in low prediction accuracy. A prediction method of mine airflow based on RBF neural network is proposed, and the parameters of RBF neural network by using particle swarm optimization algorithm are optimized. Simulation study on the prediction model by coal mine historical data is carried out. The results show that the proposed method in this paper based on improved particle swarm optimization algorithm of RBF neural network model （MPSO-RBF） has the characteristics of fast convergence rate, high precision prediction, which provides theoretical support for mine airflow temperature forecast fields.