张义民, 刘仁云, 于繁华. 基于计算智能技术的结构系统可靠性优化设计[J]. 工程力学, 2007, 24(8): 27-031,.
引用本文: 张义民, 刘仁云, 于繁华. 基于计算智能技术的结构系统可靠性优化设计[J]. 工程力学, 2007, 24(8): 27-031,.
ZHANG Yi-min, LIU Ren-yun, . COMPUTATIONAL INTELLIGENCE-BASED RELIABILITY DESIGN OPTIMIZATION FOR STRUCTURAL SYSTEM[J]. Engineering Mechanics, 2007, 24(8): 27-031,.
Citation: ZHANG Yi-min, LIU Ren-yun, . COMPUTATIONAL INTELLIGENCE-BASED RELIABILITY DESIGN OPTIMIZATION FOR STRUCTURAL SYSTEM[J]. Engineering Mechanics, 2007, 24(8): 27-031,.

基于计算智能技术的结构系统可靠性优化设计

COMPUTATIONAL INTELLIGENCE-BASED RELIABILITY DESIGN OPTIMIZATION FOR STRUCTURAL SYSTEM

  • 摘要: 利用随机摄动和Edgeworth级数方法,将非正态随机参数可靠性优化设计中的概率约束转化为等价的确定性约束,并运用粒子群算法迅速准确地获得结构系统可靠性优化设计的初始点。针对多失效模式的结构系统,提出了随机模拟-小波神经网络方法(MCS-WNN), 有效解决了结构系统的可靠性仿真。并提出了一种便于逆映射的小波神经网络模型,实现了设计参数的可靠性优化。实验结果表明上述方法是行之有效的。

     

    Abstract: The probability constraint is transferred into equivalent determinate constraint by Probabilistic Perturbation method and Edgeworth series method in reliability design optimization with non-normal random parameters. Thus, the initial design point in structural system for reliability design optimization can be obtained rapidly and accurately by Particle Swarm Algorithm. In the case of system with multi-failure modes, the Monte Carlo Stochastic-Wavelet Neural Network (MCS-WNN) method is presented. This approach can implement effectively reliability simulation of structural system. And then, an inverse mapping model of Wavelet Neural Net is presented to accomplish design parameter optimization in structural system. Experimental results show that the aforementioned methods are effective.

     

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