人工神经网络(ANNs)作为一种新的摩擦学建模技术

IF 2 Q2 ENGINEERING, MECHANICAL

Frontiers in Mechanical Engineering Pub Date : 2019-05-29 DOI:10.3389/fmech.2019.00030

I. Argatov

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引用次数: 49

摘要

本文从数学建模的角度考虑了人工神经网络。简要介绍了前馈神经网络，包括多层感知器(mlp)和径向基函数(RBF)网络。给出了它们在摩擦学研究中的应用实例，并讨论了数据驱动建模范式的重要特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Artificial Neural Networks (ANNs) as a Novel Modeling Technique in Tribology

In the present paper, artificial neural networks (ANNs) are considered from a mathematical modelling point of view. A short introduction to feedforward neural networks is outlined, including multilayer perceptrons (MLPs) and radial basis function (RBF) networks. Examples of their applications in tribological studies are given, and important features of the data-driven modelling paradigm are discussed.

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