基于小波分析及BP神经网络的瓦斯浓度预测方法研究  

Prediction Method of Gas Concentration Based on Wavelet Analysis and BP Neural Network

作　　者：赵爱蓉 

机构地区：[1]张家口学院,河北张家口075000

出　　处：《煤矿机械》2014年第6期258-260,共3页Coal Mine Machinery

基　　金：张家口市科学技术研究与发展计划项目(0811012B-4)

摘　　要：提出了一种基于小波分析与BP神经网络的矿井工作面瓦斯浓度预测算法,综合利用了小波分析算法的信号去噪作用以及BP神经网络可以拟合任何非线性系统的能力,并采用Matlab软件实现了该算法在瓦斯浓度预测上的应用。试验结果证明,对于短期内的工作面瓦斯浓度预测,该算法具有较好的预测效果。This paper puts forward a kind of prediction algorithm based on wavelet analysis and BF neural network in the prediction of gas concentration on mine working face. The algorithm combined with the wavelet analysis algorithm＇s signal denoising effect and the BP neural network can fit the ability of any nonlinear system. At last the algorithm was realized in the application of gas concentration prediction by using the Matlab software. Experimental results show that for the prediction of working face gas concentration in short term, this algorithm has better prediction effect.

关 键 词：小波分析 BP神经网络 瓦斯浓度 短期预测 

分 类 号：TD712[矿业工程—矿井通风与安全]