【摘要】：針對(duì)基于馬氏距離的重要性測度存在的問題,提出了基于譜分解加權(quán)摩爾彭羅斯馬氏距離的重要性測度指標(biāo),通過構(gòu)造多輸出協(xié)方差陣的廣義逆矩陣以及譜分解的策略,有效解決了協(xié)方差陣求逆奇異情況以及由于未能充分考慮多輸出之間的相互關(guān)系而導(dǎo)致的錯(cuò)誤識(shí)別重要變量的問題,克服了基于馬氏距離指標(biāo)的局限性。數(shù)值算例與工程算例結(jié)果表明:所提重要性測度可以更加準(zhǔn)確地獲得輸入變量對(duì)結(jié)構(gòu)系統(tǒng)多輸出性能隨機(jī)取值特征貢獻(xiàn)的排序,從而為可靠性設(shè)計(jì)提供充分的信息。
[Abstract]:In order to solve the problem of importance measure based on Markov distance, the importance measure index based on spectral decomposition weighted Moore Penrose Markov distance is proposed. The generalized inverse matrix of multi-output covariance matrix and the strategy of spectral decomposition are constructed. It effectively solves the problem of finding inverse singularity by covariance matrix and the error identification of important variables caused by failure to fully consider the interrelation between multiple outputs, and overcomes the limitation based on Markov distance index. Numerical examples and engineering examples show that the proposed importance measure can obtain more accurately the ranking of the input variables' contributions to the random characteristic values of the multi-output performance of the structural system, thus providing sufficient information for the reliability design.
【作者單位】： 西北工業(yè)大學(xué)航空學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(NSFC51475370) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(3102015BJ(Ⅱ)CG009)
【分類號(hào)】：TB114.3
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本文編號(hào)：2312629