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《Electronic Design Engineering》 2016-16
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The natural gas pipeline leak detection technology based on RBF neural network research

GAO Bing-kun;ZHENG Ren-qian;YIN Shu-xin;ZHANG Li;YUE Wu-feng;College of Electrical and Information Engineering,Northeast Petroleum University;  
In order to correctly determine whether pipeline leakage occurs,this paper adopts a hybrid learning method for network training.We set the pipeline operation parameters as the input of neural network and running status of the pipe as the neural network output,realizing the two nonlinear mapping,in order to determine whether the input signal is leakage signal,and select K-means clustering method and the recursive least square method to determine the network parameters.With the measurements of the gas pipeline operation on training and testing the RBF neural network,we get the results in an acceptable error range,which prove that the method of RBF neural network can be used for natural gas pipeline leak detection.
【Fund】: 教育部高等学校博士学科点专项科研基金(博导类)课题(20112322110003);; 黑龙江省自然科学基金面上项目(E201332);; 东北石油大学研究生创新科研项目(YJSCX2014-030NEPU)
【CateGory Index】: TE973.6;TP183
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