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首页> 外文期刊>Mechanical systems and signal processing >Simulating strongly non-Gaussian and non-stationary processes using Karhunen-Loeve expansion and L-moments-based Hermite polynomial model
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Simulating strongly non-Gaussian and non-stationary processes using Karhunen-Loeve expansion and L-moments-based Hermite polynomial model

机译：使用Karhunen-Loeve扩展和基于L-Scient的Hermite多项式模型模拟强大的非高斯和非静止过程

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摘要

A novel and efficient method is proposed for simulating strongly non-Gaussian and non-stationary processes by combining Karhunen-Loeve expansion with Linear-moments-based (L-moments-based) Hermite polynomial model (HPM). In this method, the complete transformation from non-stationary non-Gaussian auto-correlation function (ACF) to non-stationary Gaussian ACF is realized using L-moments-based HPM. Then, the underlying Gaussian processes is represented by Karhunen-Loeve expansion and further transformed into target non-stationary non-Gaussian processes by L-moments-based HPM. Moreover, a novel approach is proposed to deal with the two kinds of incompatibilities that may occur in strongly non-Gaussian processes, including that non-stationary non-Gaussian ACF falls outside of its applicable range and non-stationary Gaussian ACF is non-positive semi-definite. It can be found from some representative numerical examples that the precision and efficiency of the proposed method are considerable.
机译：提出了一种新颖的和有效的方法，用于通过将Karhunen-Loeve扩展与基于线性的矩（L-时刻的）Hermite多项式模型（HPM）组合来模拟强高斯和非静止过程。在该方法中，使用基于L-MOCENTS的HPM实现了从非静止非高斯自动相关函数（ACF）到非静止高斯ACF的完整变换。然后，基于Karhunen-Loeve扩展的基础高斯过程由基于L-MOCENTS的HPM进一步转化为目标非静止非高斯过程。此外，提出了一种新的方法，以处理可能在强大的非高斯过程中发生的两种不兼容性，包括非平稳的非高斯ACF落在其适用范围之外，非静止高斯ACF是非正面的半明确。可以从一些代表性的数字实施例中找到，所以提出方法的精度和效率是相当大的。

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