A sample-based iterative scheme for simulating non-stationary non-Gaussian stochastic processes

被引：32
|
作者：
Zheng, Zhibao [1 ,2 ]
Dai, Hongzhe [1 ,2 ]
Wang, Yuyin [1 ,2 ]
Wang, Wei [1 ,2 ]
机构：
[1] Harbin Inst Technol, Minist Educ, Key Lab Struct Dynam Behav & Control, Harbin 150090, Peoples R China
[2] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2021年 / 151卷
基金：
中国国家自然科学基金;
关键词：
Stochastic samples; Non-stationary; Non-Gaussian; Karhunen-Loeve expansion; Polynomial Chaos expansion; KARHUNEN-LOEVE EXPANSION; RANDOM-FIELDS;
D O I：
10.1016/j.ymssp.2020.107420
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
This paper presents a new numerical scheme for simulating stochastic processes specified by their marginal distribution functions and covariance functions. Stochastic samples are first generated to satisfy target marginal distribution functions. An iterative algorithm is proposed to match the simulated covariance function of stochastic samples to the target covariance function, and only a few iterations can converge to a required accuracy. Several explicit representations, based on Karhunen-Loeve expansion and Polynomial Chaos expansion, are further developed to represent the obtained stochastic samples in series forms. Proposed methods can be applied to non-stationary non-Gaussian stochastic processes, and three examples illustrate their accuracies and efficiencies. (c) 2020 Elsevier Ltd. All rights reserved.
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页数：15
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