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首页> 外文会议>International Symposium on Neural Networks pt.1; 20040819-20040821; Dalian; CN >Automatic Digital Modulation Recognition Using Wavelet Transform and Neural Networks
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Automatic Digital Modulation Recognition Using Wavelet Transform and Neural Networks

机译：小波变换和神经网络的数字调制自动识别

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摘要

This paper presents an efficient digital modulation classification method based on wavelet transform and artificial neural networks (ANN). The method performs feature extraction via the discrete wavelet transform of the underlying digital signals because of the usefulness of the wavelet in de-noising and in compressing the digital signals. The features extracted from wavelet coefficients are then presented to the ANN for pattern recognition and classification. In addition, a less nodes output player and error back propagation learning with momentum are used to speed up the training process and improve the convergence of the ANN. Experimental results and performance evaluation of the method are given and it is found that the benefits of the developed method are that its structure is simple and it performs well at low signal to noise ratio (SNR) with high overall success rates.
机译：本文提出了一种基于小波变换和人工神经网络（ANN）的有效数字调制分类方法。由于小波在去噪和压缩数字信号中的有用性，因此该方法通过基础数字信号的离散小波变换来执行特征提取。然后将从小波系数提取的特征呈现给ANN以进行模式识别和分类。此外，较少的节点输出播放器和具有动量的错误反向传播学习可用于加快训练过程并改善ANN的收敛性。给出了该方法的实验结果和性能评估，发现该方法的优点是结构简单，在低信噪比（SNR）下表现良好，总体成功率很高。

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