传感技术学报Issue(11)：1676-1681,6.DOI:10.3969/j.issn.1004-1699.2015.11.017
煤矿瓦斯涌出量动态预测的PCA-MFOA-GRNN模型及应用
Coal Mine Gas Emission Dynamic Prediction of PCA-MFOA-GRNN Model and Its Application
摘要
Abstract
For coal mine gas emission is affected by many factors,in order to overcome the complex nonlinear rela⁃tionship exists in gas emission,achieving stable,reliable and accurate dynamic prediction for the absolute amount of gas emission in fully mechanized face,then proposing the PCA and improved drosophila algorithm (MFOA) opti⁃mizing GRNN to predict the absolute amount of gas emission. Use the PCA algorithm to raw input data dimension re⁃duction and adding a trip parameter B in Si function in drosophila algorithm,to avoid local optimum factor influenc⁃ing forecasting model. Using MFOA algorithm to optimize the smoothing factor σof GRNN,establishing dynamic forecasting model for the amount of gas emission in mining face based on PCA-MFOA-GRNN algorithm,then com⁃bining with the actual mine monitoring data of gas emission to verify the model,and comparing the model predic⁃tions with FOA-GRNN algorithm uncorrected、CIPSO-ENN algorithm、BP neural network predictions and Elman network predictions,results show that:The forecasting model after improved drosophila algorithm optimizing GRNN parameter has stronger generalization ability and higher prediction accuracy than other forecasting models.关键词
动态预测/MFOA(改进的果蝇算法)/GRNN(广义回归神经网络)/PCA(主成分分析)/瓦斯涌出量Key words
dynamic prediction/MFOA (the improved Drosophila algorithm)/GRNN (Generalized regression neu-ral network)/PCA (Principal component analysis)/the amount of gas emission分类
信息技术与安全科学引用本文复制引用
皮子坤,贾宝山,贾廷贵,李锐,李宗翔..煤矿瓦斯涌出量动态预测的PCA-MFOA-GRNN模型及应用[J].传感技术学报,2015,(11):1676-1681,6.基金项目
国家自然科学基金资助项目 （）
