YangXiaobin, WangXiaoyao, ZhouShilu, ZhangZipeng. Prediction model of working face hypoxia based on improved generalized regression neural network[J]. Journal of Mining Science and Technology, 2019, 4(5): 434-440.
Citation:
YangXiaobin, WangXiaoyao, ZhouShilu, ZhangZipeng. Prediction model of working face hypoxia based on improved generalized regression neural network[J]. Journal of Mining Science and Technology, 2019, 4(5): 434-440.
YangXiaobin, WangXiaoyao, ZhouShilu, ZhangZipeng. Prediction model of working face hypoxia based on improved generalized regression neural network[J]. Journal of Mining Science and Technology, 2019, 4(5): 434-440.
Citation:
YangXiaobin, WangXiaoyao, ZhouShilu, ZhangZipeng. Prediction model of working face hypoxia based on improved generalized regression neural network[J]. Journal of Mining Science and Technology, 2019, 4(5): 434-440.
Inordertosolvetheproblemofworkingfacehypoxiaincoalminemoreeffectivelyandreasonably,animprovedgeneralneuralnetwork(GRNN)modelforpredictionofoxygenconcentrationin coalminewasconstructed,bytakingthemonitoringdataofaworkingfaceinShendongassamplesand consideringtheinteractionrelationshipbetweenphysicalparameters,basedonprincipalcomponentanalysis.Comparingthepredictedoxygenconcentrationresultswiththemeasureddata,itprovesthatthe improvedGRNN model has good fitting accuracy and generalization ability.By using the improved GRNNmodel,theoriginalGRNNmodelandBPneuralnetworkmodelrespectivelyinthecomparative analysisofhypoxiaproblems,itfoundthattheimprovedGRNNmodelhasbettereffectsandismore suitableforthepredictionofhypoxiaproblemsincoalmineface.Theinfluenceofinletairpressure, outletairpressureandinletairtemperatureontheoxygenconcentrationwereanalyzedbytheimproved GRNNmodel.ThisimprovedGRNNmodelcangiveareferencetohypoxiapredictionandhypoxiacontroltechnologyoftheworkingface.