煤矿涌水量的灰色RBF网络预测模型

为了达到准确、快速预测煤矿涌水量的目的,实现煤矿井下可靠、节能自动排水的需要,首先采用1-AGO对数据进行处理,得到规律性较强的累加数据,建立灰色预测模型,再利用径向基(RBF)神经网络对灰色预测模型结果进行预测,以作为其最终的预测值;利用某矿-600m工作面年均涌水量的历史数据进行建模,实验结果表明,灰色RBF模型在预测精度方面优于单一的灰色模型;其模型计算简便,减弱了数据的随机性及模型误差,提高了煤矿涌水量的预测精度。

Forecast Model of Mine Water Discharge Based on Grey-radial Basis Function Neural Network

In order to fast forecasting mine water discharge,realize reliable、energy conservation and automatic drainage underground coal mine,first processing data by 1-AGO,get regular strong and accumulate data,establish forecasting model,by using grey forecasting method,forecast the results of grey forecasting model by RBF Neural network,as the final forecast.Use the historical annual data of water discharge in one face for qianjiaying,experimental results show that,in forecasting precision aspects,Gray-RBF model is better than a single gray model.This model count something simple,weakened the data randomness and model error,improve the forecasting precision of mine water discharge.