【摘要】 得益于深度学习的发展，使用神经网络提升信号识别性能取得了很大进步。使用半监督方法充分利用未标记数据来辅助深度模型的训练，但是现有的半监督信号识别方法未考虑噪声的影响，因此提出了一种基于深度残差网络（Resnet）的半监督信号识别方法，并利用梯度逆转层改善了噪声对性能的影响。在开源数据集RML2016.10A、RML2016.10B和RML2016.10C上的实验结果表明，该半监督方法可借助少量标签数据信息和未标记数据来有效地训练深度模型，并且能缓解噪声对性能的影响。更多还原

【Abstract】 Benefiting from the development of deep learning, great progress has been achieved in using neural networks to improve signal recognition performance. However, most of the existing deep learning-based signal recognition methods are supervised, which requires a large amount of well-labeled data for training, but the cost of signal labeling is quite expensive. This encourages the semi-supervised methods to make full use of unlabeled data to assist the training of deep models, but existing semi-supervised signal recognition methods do not consider noise influence. Therefore, a semi-supervised signal recognition method is proposed based on deep residual network(Resnet) by using gradient reversal layers to improve noise effect on performance. Experimental results on open source datasets RML2016.10A, RML2016.10B and RML2016.10C show that the proposed semisupervised method effectively extracts discriminative features from unlabeled data by using a small amount of labeled data information, which alleviates noise influence.更多还原