基于代理模型集成学习的含时问题的可靠性分析

IF 1.7 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Multidiscipline Modeling in Materials and Structures Pub Date : 2023-08-15 DOI:10.1108/mmms-04-2023-0132
C. Zhou, Zheng Wei, Huajin Lei, Fangyun Ma, Wei Li
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引用次数: 0

摘要

目的代理模型被广泛用于替代真实模型,这些模型在时间依赖的可靠性分析中评估成本很高。通常,不同的代理模型具有不同的应用范围。然而,信息往往不足以让分析师为特定应用程序选择最合适的代理模型。因此,个体代理模型计算的结果往往是次优的,甚至是不准确的。集合模型可以有效地处理上述问题。本工作旨在研究集成模型在时间无关问题可靠性分析中的应用。设计/方法论/方法在这项工作中,开发了一种基于代理模型集成学习的时间相关问题的可靠性分析方法。代理模型的集合包括克里格、径向基函数和支持向量机。通过加权平均模型来近似预测。代理模型的集成学习是通过在整个过程中找到并添加具有大预测误差的样本点来更新的。通过实例验证了该方法的有效性。结果表明,代理模型的集成可以有效地传播时变问题的不确定性,并以较高的预测精度和计算效率评估可靠性。独创性/价值这项工作提出了一个基于代理模型集合的自适应学习框架,用于时间相关问题的不确定性传播。与单个代理模型相比,集成模型不仅节省了选择合适代理模型的工作量,尤其是在缺乏未知问题知识的情况下,而且提高了预测精度和计算效率。
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Reliability analysis of time-dependent problems based on ensemble learning of surrogate models
PurposeSurrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.Design/methodology/approachIn this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.FindingsThe effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.Originality/valueThis work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.
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来源期刊
CiteScore
3.70
自引率
5.00%
发文量
60
期刊介绍: Multidiscipline Modeling in Materials and Structures is published by Emerald Group Publishing Limited from 2010
期刊最新文献
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