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基于抛掷爆破预测的BP神经网络参数优化方法
引用本文:孙文彬,刘希亮,谭正龙,李颖,赵学胜.基于抛掷爆破预测的BP神经网络参数优化方法[J].煤炭学报,2012,37(Z1):59-64.
作者姓名:孙文彬  刘希亮  谭正龙  李颖  赵学胜
作者单位:中国矿业大学(北京) 地球科学与测绘工程学院,北京 100083
基金项目:国家自然科学基金资助项目(41171306);中央高校基本科研业务费专项资金资助项目
摘    要:研究了不同隐含节点个数、训练函数、转移函数、学习率对BP神经网络预测结果准确性和收敛速度的影响;对比分析了参数优化后的BP模型与径向基神经网络、支持向量机预测模型的结果;并应用某露天矿抛掷爆破作业的实测数据进行了相关实验。实验结果表明:最优BP模型的拓扑结构为10-6-3;最佳的训练函数为LM函数,正切和线性函数的组合为最优的转移函数,最佳的网络学习率为0.77;参数优化后BP模型的最远抛掷距离、抛掷率、松散系数的预测结果与测试样本的标准差最小,分别为9.567 8,0.036 3,0.041 4,即参数优化后的BP模型预测结果最优。

关 键 词:神经网络  预测模型  参数优化  
收稿时间:2011-10-13

Parameter optimization of BP-neural network based on the forecast of cast blasting
SUN Wen-bin,LIU Xi-liang,TAN Zheng-long,LI Ying,ZHAO Xue-sheng.Parameter optimization of BP-neural network based on the forecast of cast blasting[J].Journal of China Coal Society,2012,37(Z1):59-64.
Authors:SUN Wen-bin  LIU Xi-liang  TAN Zheng-long  LI Ying  ZHAO Xue-sheng
Affiliation:(College of Geosciences and Surveying Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China)
Abstract:A method of optimizing parameters of BP cast blasting model was described.The accuracy of predicting result and BP model convergence rate with different numbers of hidden layers,different training functions and transfer functions,different learning rates were analyzed particularly.The forecast result of parameter optimization model was contrasted with those of RBF(Radial-Basis Function),SVM(Support Vector Machine) models.The experiment was done by using a lot of surveying data of some open pit cast blasting.The result indicates that the optimizing BP topology structure is 10-6-3(10 input variables,6 hidden layers,3 output variables);LM(Levenberg-Marquart) algorithm is the best training function;tansig and purelin functions combination is the best transfer function;the best learning rate is 0.77;test sample simulation standard deviation of cast blasting ratio,max cast distance,coefficient of volumetric expansion based on parameter optimization BP model is 9.567 8,0.036 3,0.041 4 respectively and is the smallest contrasting with RBF and SVM;the prediction result of parameter optimization BP model is the best one.
Keywords:neural network  forecast model  parameter optimization
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