[1]孙晓光,周华强,何荣军.基于蚁群算法和神经网络的位移反分析[J].西安科技大学学报,2007,(04):569-572589.
 SUN Xiao-guang,ZHOU Hua-qiang,HE Rong-jun.Displacement back analysis based on ant colony algorithm and neural network[J].Journal of Xi'an University of Science and Technology,2007,(04):569-572589.
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基于蚁群算法和神经网络的位移反分析()
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2007年04期
页码:
569-572589
栏目:
出版日期:
2007-12-29

文章信息/Info

Title:
Displacement back analysis based on ant colony algorithm and neural network
文章编号:
1672-9315(2007)04-0569-04
作者:
孙晓光周华强何荣军
中国矿业大学 能源与安全工程学院,江苏 徐州 221008
Author(s):
SUN Xiao-guangZHOU Hua-qiangHE Rong-jun
School of Energy and Safety Engineering,China University of Mining and Technology,Xuzhou 221008,China
关键词:
神经网络 蚁群算法 数值模拟 力学参数
Keywords:
neural network ant colony algorithm numerical simulation mechanical parameters
分类号:
TD325
文献标志码:
A
摘要:
运用蚁群算法和人工神经网络构造了位移反分析的蚁群人工神经网络模型,并基于正交试验获得的训练样本对网络进行学习,以此训练好的神经网络模型来描述岩体力学参数和位移之间的关系。该方法以神经网络为基础,用蚁群算法来学习神经网络的权系数。利用反演结果,建立快速拉格朗日快速计算法(FLAC)模型,对地表沉陷进行预测。结果表明:用蚁群算法训练神经网络,可兼有神经网络广泛映射能力和蚁群算法快速全局收敛的性能。
Abstract:
An ACA-ANN model for displacement back analysis is founded by ant colony algorithm and artificial neural network. The network is trained with input-output data pairs obtained from numerical simulation based on the orthogonal tests. The trained network provided the relationship between mechanical parameters of the rock mass and the displacement. The method is based on the neural network, and the weighs of neural network are trained by ant colony algorithm. The inversion results were in turn used as input parameters of a FLAC model predicting the subsidence. The results show that extensive mapping ability of neural network and rapid global convergence of ant colony algorithm can be obtained by combining ant colony algorithm and neural network.

参考文献/References:

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[5] 张志军,丁德馨.位移反分析的人工神经网络方法研究[J].南华大学学报,2005,19(2):1-5.
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备注/Memo

备注/Memo:
收稿日期: 2006-08-20 作者简介: 孙晓光(1982-),男,山东烟台人,硕士,主要从事充填采矿方面的研究.
更新日期/Last Update: 2007-12-29