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基于RBF神经网络模型的结构可靠度优化方法

李刚 孟增

李刚, 孟增. 基于RBF神经网络模型的结构可靠度优化方法[J]. 应用数学和力学, 2014, 35(11): 1271-1279. doi: 10.3879/j.issn.1000-0887.2014.11.010
引用本文: 李刚, 孟增. 基于RBF神经网络模型的结构可靠度优化方法[J]. 应用数学和力学, 2014, 35(11): 1271-1279. doi: 10.3879/j.issn.1000-0887.2014.11.010
LI Gang, MENG Zeng. Reliability-Based Design Optimization With the RBF Neural Network Model[J]. Applied Mathematics and Mechanics, 2014, 35(11): 1271-1279. doi: 10.3879/j.issn.1000-0887.2014.11.010
Citation: LI Gang, MENG Zeng. Reliability-Based Design Optimization With the RBF Neural Network Model[J]. Applied Mathematics and Mechanics, 2014, 35(11): 1271-1279. doi: 10.3879/j.issn.1000-0887.2014.11.010

基于RBF神经网络模型的结构可靠度优化方法

doi: 10.3879/j.issn.1000-0887.2014.11.010
基金项目: 国家自然科学基金(11372061; 91315301)
详细信息
    作者简介:

    李刚(1966—),男,太原人,教授,博士,博士生导师(通讯作者. E-mail: ligang@dlut.edu.cn);孟增(1987—),男,甘肃陇南人,博士生(E-mail: mengz@mail.dlut.edu.cn).

  • 中图分类号: TB114.3;O213.2

Reliability-Based Design Optimization With the RBF Neural Network Model

Funds: The National Natural Science Foundation of China(11372061; 91315301)
  • 摘要: 在结构构件尺寸、材料属性以及外部载荷等不确定性因素影响下,基于可靠度的优化给出了兼顾结构的成本和安全性能的安全设计方案.由于传统的可靠度优化方法采用嵌套的双层优化列式求解,因此导致计算量过大.为了克服这个问题,学者们相继提出了解耦方法和单循环方法等方法.该文采用RBF神经网络模型用于可靠度优化问题的求解中,通过拉丁超立方方法构造代理模型,并用误差指标来验证代理模型的精确程度,同时自适应更新代理模型直至满足需求.通过与现有可靠度优化4种主流算法的比较,说明了该文提出算法的高效性和稳健性.
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出版历程
  • 收稿日期:  2014-05-15
  • 刊出日期:  2014-11-18

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