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中文摘要：
采用证据理论作为传统概率理论的替代方法来处理不精确的数据.为了降低基于证据理论不确定量化分析的计算成本,提出了基于微分演化的区间优化算法来计算边界值.以典型桁架结构的偶然不确定和认知不确定问题为例验证所提出方法的准确性和有效性.
英文摘要：
Evidence theory is proposed as an alternative to the classical probability theory to handle the imprecise data situation. In order to alleviate the computational difficulties in the evidence theory-based uncertainty quantification （UQ） analysis, a differential evolution-based interval optimization for computing bounds method is developed. A typical truss structure with the aleatory and epistemic uncertainties is investigated to demonstrate accuracy and efficiency of the proposed method.
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Usage of Comprehensive Learning Particle Swarm Optimization for ParameterIdentification of Structura
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