变压器
计算机科学
人工智能
梯度下降
嵌入
模式识别(心理学)
人工神经网络
工程类
电气工程
电压
作者
Zhibin Yue,Jianbin Lu,Lu Wan
出处
期刊:Applied Sciences
日期:2022-09-27
卷期号:12 (19): 9728-9728
被引量:1
摘要
The traditional High-Resolution Range Profile (HRRP) target recognition method has difficulty automatically extracting target deep features, and has low recognition accuracy under low training samples. To solve these problems, a ship recognition method is proposed based on the lightweight Transformer model. The model enhances the representation of key features by embedding Recurrent Neural Networks (RNN) into Transformer’s encoder. The Group Linear Transformations (GLTs) are introduced into Transformer to reduce the number of parameters in the model, and stable features are extracted through linear intergroup dimensional transformations. The adaptive gradient clipping algorithm is combined with the Stochastic Gradient Descent (SGD) optimizer to allow the gradient to change dynamically with the training process and to improve the training speed and generalization ability of the model. Experimental results on the simulated dataset show that multi-layer model stacking can effectively extract deep features of targets and raise recognition accuracy. At the same time, the lightweight Transformer model can maintain good recognition performance with low parameters and low training samples.
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