摘要
本文提出了数据驱动的非高斯随机过程模拟方法,利用人工神经网络模型建立样本转换与功率谱转换模型。基于样本数据建立了高斯样本转换到非高斯样本的神经网络模型;运用平移广义对数正态分布对样本分布函数进行建模,通过反向传播神经网络模型直接获得潜在高斯功率谱;采用谱表示法生成高斯随机过程样本,并借助样本转换神经网络模型将其转换为非高斯过程样本。这种方法能够在有限样本数据的基础上生成非高斯随机过程样本,解决了传统转换模型精度欠佳、适用范围受限以及难以确定潜在高斯功率谱等难题。通过数值算例和脉动风场模拟验证,进一步证明了所提方法的准确性和有效性。
A novel data-driven method for simulating non-Gaussian stochastic processes is proposed in this paper.The sample conversion.model and power spectrum conversion model are established by using artificial neural network models respectively.A neural network model is constructed based on sample data to transform Gaussian samples into non-Gaussian samples.The distribution function of the samples is modeled using the shifted generalized lognormal distribution,and the latent Gaussian power spectrum is directly obtained through the backpropagation neural network model.The Gaussian stochastic process samples are generated using the spectral representation method,and then transformed into non-Gaussian process samples using the sample conversion neural network model.This method is capable of generating non-Gaussian stochastic process samples based on limited sample data,addressing the challenge of determining latent Gaussian power spectrum,and solving the problems such as poor accuracy and limited application range of the central moments-based transformation models.Through numerical simulations and validation in turbulent wind fields,the accuracy and effectiveness of the proposed method are further demonstrated.
作者
李扬
徐军
LI Yang;XU Jun(College of Civil Engineering,Hunan University,Changsha 410082,China;Key Lab on Damage Diagnosis for Engineering Structures of Hunan Province,Changsha 410082,China)
出处
《振动工程学报》
北大核心
2025年第9期1995-2001,共7页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(52278178,51978253)。
关键词
随机过程
非高斯
神经网络
谱表示法
转换过程
功率谱
stochastic process
non-Gaussian
neural network
spectral representation method
translation process
power spectrum
