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

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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Bibliographic Details
Main Authors: Zhiyuan Dai, Tian Li, Ze-Rui Xiang, Weihua Zhang, Jiye Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Feedforward neural network
sample expansion strategy
multi-objective optimization
high-speed train
train nose shape
aerodynamic characteristic
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2023.2226187

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https://www.tandfonline.com/doi/10.1080/19942060.2023.2226187

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