爆破振动特征参量的粗糙集模糊神经网络预测
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摘要
爆破振动特征参量对爆破振动危害效应有重要影响。首次用粗糙集模糊神经网络方法对振幅、主频率及主频持续时间进行预测。首先介绍了粗糙集模糊神经网络的基本思想,其次,分析了印象爆破振动特征参量的主要因素,建立了基于粗糙集模糊神经网络的爆破振动特征参量预测模型;最后用某边坡开挖爆破中的振动观测指标对模型进行了训练,并对15组指标进行了测试。结果表明:粗糙集模糊神经网络预测模型能反映了影响因素与特征量之间的非线性关系,适用于爆破振动特征参量预测。一次预测1个指标的精度高于同时预测3个指标的精度。
Characteristic variables of blasting vibration have great efiects on its damage level.The prediction of characteristic variables caused by blasting vibration is helpful to study blasting vibration effect.Here,the prediction of the amplitude and the first dominant frequency band and its time duration were achieved on the basis of rough set and fuzzy neural network(FNN)theory.The purpose of this study was to explore a method which could avoid the limitation of the prediction with only one index and to improve the prediction precision.Firstly,the drawback of the prediction of the am-plitude based on Sadov's vibration formula was analyzed.Secondly,rough set and fuzzy neural network(RSFNN)theory were introduced briefly.Thirdly,a rough set-based FNN prediction model for characteristic variables of blasting vibration was established based on analysis of factors affecting blasting vibration characteristic variables.Finally,the model was trained with data come from Tonglvshan Copper Mine and was tested by 1 5 groups of data.The results showed that the rough set and FNN prediction model reflected the nonlinear relationship between factors and characteristic variables and could be used to predict characteristic variables of blasting vibration.It was also found that the precision of predicting sin-gle index a time was higher than that of predicting three indexes at the same time.
Characteristic variables of blasting vibration have great efiects on its damage level.The prediction of characteristic variables caused by blasting vibration is helpful to study blasting vibration effect.Here,the prediction of the amplitude and the first dominant frequency band and its time duration were achieved on the basis of rough set and fuzzy neural network(FNN)theory.The purpose of this study was to explore a method which could avoid the limitation of the prediction with only one index and to improve the prediction precision.Firstly,the drawback of the prediction of the am-plitude based on Sadov's vibration formula was analyzed.Secondly,rough set and fuzzy neural network(RSFNN)theory were introduced briefly.Thirdly,a rough set-based FNN prediction model for characteristic variables of blasting vibration was established based on analysis of factors affecting blasting vibration characteristic variables.Finally,the model was trained with data come from Tonglvshan Copper Mine and was tested by 1 5 groups of data.The results showed that the rough set and FNN prediction model reflected the nonlinear relationship between factors and characteristic variables and could be used to predict characteristic variables of blasting vibration.It was also found that the precision of predicting sin-gle index a time was higher than that of predicting three indexes at the same time.
引文
[1]张义平,李夕兵,赵国彦,等.爆破震动信号的时频分析[J].岩土工程学报,2005,27(12):1472-1476.
[2]李夕兵,张义平,刘志祥,等.爆破震动信号的小波分析与HHT变换[J].爆炸与冲击,2005,25(6),528-534.
[3]闫鸿浩,李晓杰,曲艳东,等.爆破振动速度测试精细分析[J].岩土力学,2007,28(10):2091-2094.
[4]凌同华,李夕兵,王桂尧.爆破震动灾害主动控制方法研究[J].岩土力学,2007,28(7):2091-2094.
[5]凌同华,李夕兵.单段爆破振动信号频带能量分布的小波包分析[J].振动与冲击,2007,26(5):41-43.
[6]李宏男,王丙乾,林皋.爆破地震效应若干问题的研究[J].爆炸与冲击,1996,16(1):61-62.
[7]林大超,施惠基,白春华,等.爆炸地震效应的时频分析[J].爆炸与冲击,2003,23(1):31-36.
[8]周春华;龙源;晏俊伟,等.基于WTMM的爆破振动信号奇异性分析[J].振动与冲击,2007,26(1):108-110.
[9]Dave Lilly.Neural network simulation[J].Charting the fu-ture of blast process control.ISEE.Volume13/No.2Sep./Oct.1995.
[10]XU Quanjun.Ground vibration model testing of bench blas-ting and BP neural network analysis.The7th international symposium on rock fragmentation by blasting,Beijing/China/11-15,August2002:591-594.
[11]Zhong Dongwan.Study on the forecast of blasting vibration peak value based on the fuzzy neural network.The7th inter-national symposium on rock fragmentation by blasting,Bei-jing/China/11-15August2002:263-266.
[12]张燕秦,徐向东.用于热力系统建模的基于粗糙集的模糊神经网络[J].清华大学学报(自然科学版),2004,44(8):1083-1086.
[2]李夕兵,张义平,刘志祥,等.爆破震动信号的小波分析与HHT变换[J].爆炸与冲击,2005,25(6),528-534.
[3]闫鸿浩,李晓杰,曲艳东,等.爆破振动速度测试精细分析[J].岩土力学,2007,28(10):2091-2094.
[4]凌同华,李夕兵,王桂尧.爆破震动灾害主动控制方法研究[J].岩土力学,2007,28(7):2091-2094.
[5]凌同华,李夕兵.单段爆破振动信号频带能量分布的小波包分析[J].振动与冲击,2007,26(5):41-43.
[6]李宏男,王丙乾,林皋.爆破地震效应若干问题的研究[J].爆炸与冲击,1996,16(1):61-62.
[7]林大超,施惠基,白春华,等.爆炸地震效应的时频分析[J].爆炸与冲击,2003,23(1):31-36.
[8]周春华;龙源;晏俊伟,等.基于WTMM的爆破振动信号奇异性分析[J].振动与冲击,2007,26(1):108-110.
[9]Dave Lilly.Neural network simulation[J].Charting the fu-ture of blast process control.ISEE.Volume13/No.2Sep./Oct.1995.
[10]XU Quanjun.Ground vibration model testing of bench blas-ting and BP neural network analysis.The7th international symposium on rock fragmentation by blasting,Beijing/China/11-15,August2002:591-594.
[11]Zhong Dongwan.Study on the forecast of blasting vibration peak value based on the fuzzy neural network.The7th inter-national symposium on rock fragmentation by blasting,Bei-jing/China/11-15August2002:263-266.
[12]张燕秦,徐向东.用于热力系统建模的基于粗糙集的模糊神经网络[J].清华大学学报(自然科学版),2004,44(8):1083-1086.
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