Aerodynamic multi-objective optimization on train nose shape using feedforward neural network and sample expansion strategy

Feedforward neural network (FNN) models with strong learning ability and prediction accuracy are crucial for optimization. This paper investigates the effects of the number of training samples and the hidden layers on the accuracy of the FNN model. Meanwhile, under the premise of a high space-fillin...

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Автори:	Zhiyuan Dai, Tian Li, Ze-Rui Xiang, Weihua Zhang, Jiye Zhang
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Taylor & Francis Group 2023-12-01
Серія:	Engineering Applications of Computational Fluid Mechanics
Предмети:	Feedforward neural network sample expansion strategy multi-objective optimization high-speed train train nose shape aerodynamic characteristic
Онлайн доступ:	https://www.tandfonline.com/doi/10.1080/19942060.2023.2226187
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