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自适应重要抽样方法的改进算法 被引量:5

IMPROVED ADAPTIVE IMPORTANCE SAMPLING ALGORITHM
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摘要 失效概率的计算是结构可靠度分析的核心问题之一,发展精确高效的失效概率估计方法渐成国际学术与工程界关注的焦点。该文提出了一种基于样本概率密度加权的采样中心确定方法,该方法兼顾了以下2个目标:1)增加有效抽样中对失效概率贡献大的样本出现的概率;2)提高有效抽样比例。通过将该方法与基于主动引导技术的自适应抽样方法相集成,得到了一种改进的自适应重要抽样方法。理论分析与数值算例表明:该文提出的自适应重要抽样算法具有精度高、计算量小的优点。 Structural reliability analysis requires an accurate and efficient evaluation for failure probability. A new scheme is proposed to identify the optimal importance sampling center, based on weighing samples by their probability density functions (PDF). By incorporating the scheme into the adaptive importance sampling method ISAG (adaptive Importance Sampling based on Active Guiding technology), an improved adaptive importance sampling method is proposed. The accuracy and efficiency of the proposed method is demonstrated and verified by two numerical experiments.
出处 《工程力学》 EI CSCD 北大核心 2012年第11期123-128,共6页 Engineering Mechanics
基金 国家"十一五"科技支撑项目(2006BAJ01B01-2)
关键词 结构可靠度 自适应 重要抽样 最优抽样中心 主动引导 structural reliability adaptive importance sampling optimal sampling center active guiding
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参考文献9

  • 1Melchers R E. Structural reliability analysis and prediction [M]. 2nd ed. New York: J Wiley & Sons, 1999: 64- 93.
  • 2董聪,郭晓华.基于广义遗传算法的自适应重要抽样理论[J].计算机科学,2000,27(4):1-4. 被引量:2
  • 3Bucher C G. Adaptive sampling-an iterative fast Monte Carlo procedure [J]. Structural Safety, 1988, 5(2): 119- 126.
  • 4Au S K, Beck J L. A new adaptive importance sampling scheme [J]. Structural Safety, 1999, 21(2): 135- 158.
  • 5Au S K, Beck J L. Estimation of small failure probabilities in high dimensions by subset simulation [J]. Probabilistic Engineering Mechanics, 2001, 16(4): 263- 277.
  • 6Katafygiotis L S, Zuev K M. Geometric insight into the challenges of solving high-dimensional reliability problems [J]. Probabilistic Engineering Mechanics, 2008, 23(2/3): 208-218.
  • 7Valdebenito M A, Pradlwarter H J, SchuEller G I. The role of the design point for calculating failure probabilities in view of dimensionality and structural nonlinearities [J]. Structural Safety, 2010, 32(2): 101- 111.
  • 8Robert C P, Casella G. Monte carlo statistical methods [M]. 2nd ed. New York: Springer-Verlag, 2004: 90-106, 267-299.
  • 9Press W H, Teukolsky S A, Vetterling W T, Flannery B P. Numerical recipes-the art of scientific computing [M]. 3rd ed. New York: Cambridge University Press, 2007: 487-555.

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