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

被引：64
作者：
Argatov, Ivan [1 ]
机构：
[1] Tech Univ Berlin, Inst Mech, Dept Syst Dynam & Phys Frict, Berlin, Germany
来源：
FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND | 2019年 / 5卷
关键词：
artificial neural network; data-driven modeling; tribological properties; wear; fretting; WEAR; PREDICTION; FRICTION; BEHAVIOR; VALIDATION; SIMULATION; VARIABLES; CONTACT; SYSTEM;
D O I：
10.3389/fmech.2019.00030
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In the present paper, artificial neural networks (ANNs) are considered from a mathematical modeling 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 modeling paradigm are discussed.
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页数：9
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