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《Chinese Journal of Sensors and Actuators》 2012-01
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Application of QGA-RBF for Predicting the Amount of Mine Gas Emission

WANG Tao,WANG Yangyang,GUO Changna,ZHANG Jihua(Electrical Control Engineering institute of Liaoning Technology University,Huludao Liaoning 125105,China)  
Safe accidents in coal mine are aroused more than 80 percent by the excess of gas.In order to make gas emission quantity forecasting result more accurate and guarantee the safety of coal mine,detecting sufficient and typical gas data are collected as samples in this paper.The quantum genetic algorithm is adopted to optimize the parameters of Radial Basis Function neural networks,and the forecasting model used for carrying out the gas emission quantity forecasting is established.The simulating result obtained by using Matlab indicates the optimized forecasting model has an more accuracy forecasting result than the forecasting model based on RBF neural networks.A theoretical basis is provided for the prevention and control of gas accidents in coal mine.
【Fund】: 辽宁教育厅高等学校科研计划项目(2009A351)
【CateGory Index】: TD712.5;TP183
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