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Epoch determination for neural network by self-organized map (SOM)

机译:通过自组织映射(SOM)确定神经网络的时代

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摘要

Artificial neural networks have a wide application in many areas of science and engineering and, particularly, in geotechnical problems with some degree of success due to the fact that the mechanical behavior of rocks are not salient. They are highly nonlinear, quite complex and complicated. While applying neural network in such complicated problems, epoch determination is based on hit-and-trail basis mainly. In this paper, the effect of different number of epochs is shown on the network and a method is proposed to determine the optimum number of epoch with the help of self-organized map (SOM) to avoid overtraining of the network. Data distribution is also done with the help of SOM and a statistical analysis is made to show consistency between training and testing dataset for ensuring the optimal model performance.
机译:人工神经网络在科学和工程的许多领域中都有广泛的应用,尤其是在岩土问题方面,由于岩石的机械行为不显着,这一点取得了一定程度的成功。它们是高度非线性的,相当复杂和复杂。在将神经网络应用于此类复杂问题时,历时确定主要基于“一触即发”的基础。本文在网络上展示了不同时期的影响,并提出了一种借助自组织映射(SOM)确定最佳时期的方法,以避免网络过度训练。数据分发还借助SOM进行,并进行统计分析以显示训练和测试数据集之间的一致性,以确保最佳模型性能。

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