| 桥梁断面静力三分力系数的人工神经网络识别 |
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| 引用本文： | 李林,李乔,廖海黎.桥梁断面静力三分力系数的人工神经网络识别[J].西南交通大学学报,2004,39(6):740-743,757. |
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| 作者姓名： | 李林 李乔 廖海黎 |
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| 作者单位： | 西南交通大学土木工程学院,四川,成都,610031 |
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| 基金项目： | 四川省学术带头人基金资助项目 |
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| 摘 要： | 通过风洞模型试验得到了足够的样本，在此基础上利用MATLAB神经网络工具箱构造了2个BP人工神经网络；采用BR(Bayesian regularization)算法，比较了不同坐标系下的静力三分力系数的训练结果，得出4层网络比较有效且具有较高精度的结论．最后，提出了应用人工神经网络需要注意的问题。
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| 关 键 词： | 静力三分力系数 BR（Bayesian regularization）算法 BP人工神经网络 |
| 文章编号： | 0258-2724(2004)06-0740-05 |
Identification of Static Coefficients of Bridge Section with Artificial Neural Network |
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| LI Lin,LI Qiao,LIAO Hai-li.Identification of Static Coefficients of Bridge Section with Artificial Neural Network[J].Journal of Southwest Jiaotong University,2004,39(6):740-743,757. |
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| Authors: | LI Lin LI Qiao LIAO Hai-li |
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| Abstract: | Based on enough samples obtained by model experiments in wind tunnel, two BP artificial neural networks (ANNs) were constructed with the MATLAB toolbox of ANN. Then the two ANNs were used to train static coefficients in two different coordinate systems. i.e. body and wind coordinate systems, and training results of static coefficients of bridge section in the two coordinate systems were compared using the Bayesian regularization algorithm. The result shows that a four-layer network is more efficient and has better accuracy. Finally, some problems to the application of ANNs to the identification were pointed out. |
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| Keywords: | static coefficients Bayesian regularization algorithm BP artificial neural network |
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