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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2023.2226187 |