There has been a lot of work done on the analysis of Gaussian loading analysis perhaps because its occurrence is more common than non-Gaussian loading problems. It is nevertheless known that non-Gaussian load occurs in many instances especially in various forms of transport, land, sea and space. Part of the challenge with non-Gaussian loading analysis is the increased number of variables that are needed to model the loading adequately. Artificial neural network approach provides a versatile means to develop models that may require many input variables in order to achieve applicable predictive generalisation capabilities. Artificial neural network has been shown to perform much better than existing frequency domain methods for random fatigue loading under stationary Gaussian load forms especially when mean stress effects are included. This paper presents an artificial neural network model with greater predictive capability than existing frequency domain methods for both Gaussian and non-Gaussian loading analysis. Both platykurtic and leptokurtic non-Gaussian loading cases were considered to demonstrate the scope of application. The model was also validated with available SAE experimental data, even though the skewness and kurtosis of the signal in this case were mild.

中文翻译：

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用于高斯和非高斯随机疲劳载荷分析的人工神经网络

关于高斯加载分析的分析工作已经做了很多，可能是因为它的出现比非高斯加载问题更常见。然而，众所周知，非高斯载荷在许多情况下发生，尤其是在各种形式的运输、陆地、海洋和太空中。非高斯载荷分析的部分挑战是充分模拟载荷所需的变量数量增加。人工神经网络方法提供了一种通用方法来开发可能需要许多输入变量以实现适用的预测泛化能力的模型。人工神经网络已被证明比现有频域方法在平稳高斯载荷形式下的随机疲劳载荷方面表现更好，尤其是在包括平均应力效应时。本文提出了一种人工神经网络模型，其预测能力比现有的高斯和非高斯载荷分析的频域方法更强。platykurtic 和leptokurtic 非高斯载荷情况都被认为是证明应用范围。该模型还使用可用的 SAE 实验数据进行了验证，即使在这种情况下信号的偏度和峰度是轻微的。