基于多变量非高斯随机过程间的相关性，将发展的单变量非高斯过程自回归和自回归滑动平均（AR和ARMA）模型模拟算法扩展至多变量非高斯过程的数值模拟。通过AR和ARMA模型系数考虑多变量非高斯过程间的相关性，建立多变量非高斯过程AR和ARMA模型的模拟算法。多变量非高斯风压的数值模拟表明：AR和ARMA模型算法能有效地模拟低斜度、中斜度和高斜度的多变量非高斯随机过程。
Abstract
Based on the correlativity of multivariate non-Gaussian random processes, autoregressive (AR) and autoregressive moving average (ARMA) models proposed for simulating a univariate non-Gaussian stochastic process were extended to simulate multivariate non-Gaussian stochastic processes. Through using coefficients of AR and ARMA models to consider the correlativity of multivariate non-Gaussian processes, the simulation algorithm for multivariate non-Gaussian processes was established with AR and ARMA models. The numerical simulations for multivariate non-Gaussian fluctuating wind pressure indicated that the new simulation algorithm with AR and ARMA models can effectively simulate multivariate non-Gaussian random processes with low skewness, middle one, and high one, respectively.
关键词
多变量非高斯随机过程 /
非高斯脉动风压 /
自回归模型 /
自回归滑动平均模型
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Key words
multivariate non-Gaussian random processes /
non-Gaussian fluctuating wind pressure /
autoregressive (AR) model /
autoregressive moving average (ARMA) model
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脚注
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