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A study on the fatigue damage model for Gaussian wideband process of two peaks by an artificial neural network

机译：基于人工神经网络的两峰高斯宽带过程疲劳损伤模型研究。

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Calculations of the fatigue damage on marine structures with a wideband nature are difficult to be done in spectral approach point of view because the link between the spectrum of stress and the probability distribution is difficult to define. This paper addresses the methodology through which the functional relationship between the probability density function and the response spectrum of a bimodal wideband process by using the artificial neural network technique. An artificial neural network scheme was used to identify the multivariate functional relationship between the two continuously varying functions. For this, the spectra were idealized as the superposition of two triangles with an arbitrary location, height and width and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, a variety of different wide-band spectra were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. It turned out that the network trained using the given data set could reproduce the probability density function of an arbitrary wide-band spectrum of two triangles with great success. (C) 2015 Elsevier Ltd. All rights reserved.
机译：从光谱逼近的角度来看，很难对具有宽带性质的海洋结构进行疲劳损伤的计算，因为应力谱与概率分布之间的联系很难定义。本文通过人工神经网络技术，探讨了概率密度函数与双峰宽带过程响应谱之间的函数关系的方法。使用人工神经网络方案来识别两个连续变化的函数之间的多元函数关系。为此，将光谱理想化为两个具有任意位置，高度和宽度的三角形的叠加，并且概率密度函数由等距高斯基函数的线性组合表示。为了在监督下训练网络，假定了各种不同的宽带频谱，并使用雨流计数方法得出了应力范围的收敛概率密度函数，并将所有这些数据集输入到三层感知器模型中。事实证明，使用给定数据集训练的网络可以成功复制两个三角形的任意宽带频谱的概率密度函数。 （C）2015 Elsevier Ltd.保留所有权利。

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