摘要
提出了一种基于径向基函数(RBF)神经网络的通信信号调制识别方法,该方法采用模糊C-均值(FCM)聚类算法对数据进行聚类,并获取基函数的参数,采用梯度下降法训练网络权值．利用最优停止法对网络进行了优化,避免了过学习现象,提高了RBF网络的训练速度和泛化能力,以实际信号数据对该网络进行性能检验,实验结果表明了该RBF网络具有较高的识别精度．
In this paper, a novel modulation recognition method is proposed, which is based on an improved radial basis function （RBF） neural network. The parameters of radial basis function are obtained by fuzzy G-means （FGM） clustering algorithm, while weights of the network are trained with gradient descent approach. Optimal stopping rule is used to avoid overfitting and improve training speed as well as generalization ability. Application of this method to modulation recognition of practical signals shows satisfactory performance.
出处
《自动化学报》
EI
CSCD
北大核心
2007年第6期652-654,共3页
Acta Automatica Sinica
