|本期目录/Table of Contents|

[1]高宗军,付青,郑秋霞,等.BP和Elman神经网络在砂土液化预测中的研究[J].中国安全生产科学技术,2013,9(6):58-62.[doi:10.11731/j.issn.1673-193x.2013.06.011]
　GAO Zong jun,FU Qing,ZHENG Qiu xia,et al.Study on forecasting of sand liquefaction by using BP neural and Elamn neural networks[J].Journal of Safety Science and Technology,2013,9(6):58-62.[doi:10.11731/j.issn.1673-193x.2013.06.011]
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BP和Elman神经网络在砂土液化预测中的研究

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
9
期数:
2013年6期
页码:
58-62
栏目:
职业安全卫生管理与技术
出版日期:
2013-06-30

文章信息/Info

Title:
Study on forecasting of sand liquefaction by using BP neural and Elamn neural networks
作者:
高宗军; 付青; 郑秋霞; 王世臣; 许传杰; 董红志
（山东省沉积成矿作用与沉积矿产重点实验室/山东科技大学地质科学与工程学院，山东青岛266590）
Author(s):
GAO Zong jun; FU Qing;  ZHENG Qiu xia; WANG Shi chen; XU Chuan jie; DONG Hong zhi
(Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals, College of GeologicalSciences & Engineering, Shandong University of Science and Technology, Qingdao Shandong 266590, China)
关键词:
砂土液化; BP神经网络;  Elman神经网络; Matlab软件
Keywords:
sand liquefaction;  BP neural networks;  elamn neural networks;  Matlab software
分类号:
P642
DOI:
10.11731/j.issn.1673-193x.2013.06.011
文献标志码:
A
摘要:
基于砂土液化的影响因素具有非线性关系，而神经网络模型能够逼近任意非线性函数和适合于动态系统辨识的特性，分别建立输入层为4，隐含层神经元为2，输出层为1的三层BP神经网络和Elman网络，并且通过matlab软件运算，实例比较得出Elman模型比BP模型收敛速度快、精度高，在砂土液化的预测中效果更好。
Abstract:
Based on the condition that the influencing factors of sand liquefaction have a nonlinear relationship, while the neural network model can simulate any nonlinear function and is suitable for dynamic system recognition, in this paper, a threelayer BP neural network and Elman network with 4 input layer neuron ,2 hidden layer neuron and 1output layer neuron were established respectively. What’s more, by the way of practical examples simulations, Elamn neural networks haa a faster convergence speed, a higher accuracy and a better effect than Elamn neural networks.

参考文献/References:

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备注/Memo

备注/Memo:
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更新日期/Last Update: 2013-06-30