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Discretization Analysis Method of Hybrid Reliability Based on Evidence Theory

机译:基于证据理论的混合可靠性离散化分析方法

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Aiming at the problem that various types of uncertainties, such as randomness, fuzziness, and interval, coexist in structure reliability analysis, a discretization analysis method of hybrid reliability for uncertain structures is proposed based on evidence theory (ET) in this article. Firstly, in order to establish a hybrid reliability model based on ET, a generalized density method (GDM) is developed to transform the fuzzy variables into equivalent random variables on the basis of the entropy equivalent method (EEM). Based on the discrete property of the basic probability assignment (BPA) in evidence theory, the random variables and fuzzy variables (equivalent random variables) are both discretized into subintervals according to six-sigma rule. Then, the BPA of each subinterval is solved and all focal elements are assigned BPA, so the evidence structure characterization of random and fuzzy variables is realized. Secondly, using Fmincon function based on the sequential quadratic programming (SQP) algorithm in MATLAB, the minimum and maximum values of performance function over each focal element can be acquired directly. Meanwhile, the production rules are used to judge the belonging of focal elements and classify them, so the numerical calculation of belief measure and plausibility measure is also realized. Finally, combined with the Monte Carlo Simulation (MCS) method, an engineering example is provided to demonstrate the feasibility and accuracy of the proposed method.
机译:针对结构可靠性分析中随机性,模糊性和区间性等不确定性共存的问题,提出了一种基于证据理论的不确定结构混合可靠性离散化分析方法。首先,为了建立基于ET的混合可靠性模型,提出了一种通用密度法(GDM),在熵等效法(EEM)的基础上将模糊变量转换为等效随机变量。基于证据理论中基本概率分配(BPA)的离散属性,随机变量和模糊变量(等效随机变量)均根据6-sigma规则离散化为子间隔。然后,求解每个子区间的BPA,并为所有焦点元素分配BPA,从而实现随机变量和模糊变量的证据结构表征。其次,使用基于MATLAB中顺序二次编程(SQP)算法的Fmincon函数,可以直接获取每个焦点元素上性能函数的最小值和最大值。同时,利用生产规则判断震源要素的归属并对其进行分类,从而实现了置信度和合理度的数值计算。最后,结合蒙特卡罗模拟(MCS)方法,提供了工程实例,证明了该方法的可行性和准确性。

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