A new evidence-theory-based method for response analysis of acoustic system with epistemic uncertainty by using Jacobi expansion
A new evidence-theory-based method for response analysis of acoustic system with epistemic uncertainty by using Jacobi expansion
复制标题
基于证据理论的雅可比展开认知不确定性声学系统响应分析新方法
DOI：
10.1016/j.cma.2017.04.020
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发表时间：
2017-08
影响因子：
7.2
通讯作者：
Xia Baizhan
中科院分区：
文献类型：
--
作者：
Yin Shengwen;Yu Dejie;Yin Hui;Xia Baizhan
Evidence theory has strong ability to handle epistemic uncertainties whose precise probability distributions cannot be obtained due to limited information. However, the excessive computational cost produced by repetitively extreme value analysis severely influences the practical application of evidence theory. This paper aims to develop an efficient algorithm for epistemic uncertainty analysis of acoustic problem under evidence theory. Based on the orthogonal polynomial approximation theory, a numerical approach named as theevidence-theory-based Jacobi expansion method(ETJEM) is proposed. In ETJEM, the response of acoustic system with evidence variables is approximated by Jacobi expansion, through which the repetitively extreme value analysis needed in evidence theory can be efficiently performed. The parametric Jacobi polynomial of Jacobi expansion holds a large number of polynomials as special cases, such as the Legendre polynomial and Chebyshev polynomial. Thus, the ETJEM permits a much wider choice of polynomial bases to control the error of approximation than the traditional evidence-theory-based orthogonal polynomial approximation method, in which only the Legendre polynomial is used for approximation. Three numerical examples are employed to demonstrate the effectiveness of the proposed methodology, including a mathematic problem with explicit expression and two engineering applications in acoustic field. In these three numerical examples, efficiency and accuracy are fully studied by comparing with Legendre expansion method as well as Monte Carlo simulations.
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影响因子：
--
作者：
ZADEH, LA
通讯作者：
ZADEH, LA
不确定性下非线性动力系统的切比雪夫区间法
影响因子：
5
作者：
Wu, Jinglai;Zhang, Yunqing;Chen, Liping;Luo, Zhen
通讯作者：
Luo, Zhen
DOI：
10.1049/pbpo161e_ch3
发表时间：
2021-07
期刊：
Artificial Intelligence for Smarter Power Systems: Fuzzy logic and neural networks
影响因子：
--
作者：
通讯作者：
--
DOI：
10.2514/6.2009-2274
发表时间：
2009-05
期刊：
--
影响因子：
--
作者：
M. Eldred
通讯作者：
M. Eldred
影响因子：
4.7
作者：
Menghui Xu;Z. Qiu;Xiaojun Wang
通讯作者：
Menghui Xu;Z. Qiu;Xiaojun Wang