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中文摘要：
为了提高可靠性分析的精度,提出了一种改进的基于概率加权矩的无模型抽样方法。该方法同时以样本的概率加权矩和经验分布函数为约束条件来产生大样本,从而保证了目标样本局部统计信息和全局统计信息与原始观测样本信息的一致。所提方法还对目标样本初始点的选择方法进行了改进,使得目标样本能更快收敛于真解。数值算例验证表明,在拟合经验分布函数时,基于概率加权矩的无模型抽样在局部统计信息、全局统计信息的一致性以及收敛速度方面均优于传统的无模型抽样。
英文摘要：
In order to improve the agreement of the statistical information of a target sample with that of observed original sample both globally and locally,a novel model-free sampling was developed,in which the constraints of the probability weighted moments and the empirical cumulative distribution function（CDF） were satisfied simultaneously.The probability weighted moment constraint guarantees the agreement of the local statistical information and the empirical CDF guarantees that of the global statistical information.Additionally,the selection of the initial target sample was improved for the convergence of the novel model-free sampling.The numerical examples demonstrate the advantages of the novel model-free sampling over the traditional one.
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