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- Title
一种优化孪生网络的小样本辐射源个体识别方法.
- Authors
梁先明
- Abstract
For the problems that the signal emitter individual identification of small samples is difficult to stably converged and the recognition accuracy is low, a network model based on optimization of siamese networks for small sample emitter individual identification is proposed. The siamese networks similar to the Spring Model are analyzed, which can increase the distance of feature vectors of dissimilar paris, decrease the distance of feature vectors of similar paris, and realize the fast convergence with small sample training loss. Then cross entropy is used to optimize the loss function to improve the accuracy and stability of small sample identification. The experimental result shows that the individual recognition accuracy of the proposed method can reach 88% for each communication station with no more than 10 training samples.
- Subjects
ARTIFICIAL neural networks; ENTROPY; IDENTIFICATION
- Publication
Telecommunication Engineering, 2022, Vol 62, Issue 6, p695
- ISSN
1001-893X
- Publication type
Article
- DOI
10.3969/j.issn.1001-893x.2022.06.001