| Abstract: | Neural networks are being used to construct meta-models in numerical simulation of structures. In addition to network structures
and training algorithms, training samples also greatly affect the accuracy of neural network models. In this paper, some existing
main sampling techniques are evaluated, including techniques based on experimental design theory, random selection, and rotating
sampling. First, advantages and disadvantages of each technique are reviewed. Then, seven techniques are used to generate
samples for training radial neural networks models for two benchmarks: an antenna model and an aircraft model. Results show
that the uniform design, in which the number of samples and mean square error network models are considered, is the best sampling
technique for neural network based meta-model building.
Supported by: Specialized Research Fund for the Doctoral Program of Higher Education, China (No. 20010227012) |
