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Evidence-theory-based uncertain parameter identification method for mechanical systems with imprecise information

机译：基于证据理论的不确定参数识别方法，但是具有不精确信息的机械系统

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摘要

In many mechanical engineering practices, the sample information is usually imprecise due to the complex objective environment or various subjective cognitions. In this study, a kind of inverse problem for identifying the uncertain system parameters with imprecise information is investigated by using the evidence theory. First, the uncertain input parameters to be identified are approximately characterized by evidence variables with subinterval-type focal elements. Through the optimization procedure executed in the given computational model, the output response can be expressed as a group of interval numbers with basic probability assignment (BPA). In the subsequent inverse analysis framework, by cumulating the imprecise experimental response measurements with belief degrees to update the response BPAs, the related interval range of unknown evidence variables can be gradually calibrated toward the true value. To improve the optimization efficiency of output response calculation with respect to various focal elements, a relatively simple metamodel is established as an alternative of the original computational model, where the Legendre-type polynomial and Clenshaw-Curtis point are respectively utilized as the basis function and sample construction strategy. Eventually, numerical results in two examples verify that the uncertain parameter identification can be effectively achieved by the presented method. (C) 2019 Elsevier B.Y. All rights reserved.
机译：在许多机械工程实践中，由于复杂的目标环境或各种主观认知，样本信息通常是不精确的。在本研究中，通过使用证据理论研究了一种用于识别不确定信息的不确定系统参数的逆问题。首先，要识别的不确定输入参数近似是具有子内型焦点元素的证据变量的特征。通过在给定的计算模型中执行的优化过程，输出响应可以表示为具有基本概率分配（BPA）的一组间隔数。在随后的逆分析框架中，通过将不精确的实验响应测量与信仰度进行累积以更新响应BPA，可以逐渐校准未知证据变量的相关区间范围，以朝着真实值逐渐校准。为了提高关于各种焦点元件的输出响应计算的优化效率，建立了相对简单的元模型作为原始计算模型的替代方案，其中图例型多项式和CLENShaw-Curtis点分别用作基本函数和样本施工策略。最终，两个示例中的数值结果验证了通过呈现的方法可以有效地实现不确定参数识别。 （c）2019年Elsevier B.Y.版权所有。

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