| 一种优化的RBF神经网络在调制识别中的应用 |
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| 引用本文： | 叶健,葛临东,吴月娴.一种优化的RBF神经网络在调制识别中的应用[J].自动化学报,2007,33(6):652-654. |
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| 作者姓名： | 叶健 葛临东 吴月娴 |
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| 作者单位： | 1.解放军信息工程大学 郑州 450002 |
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| 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.
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| 关 键 词： | 调制识别 径向基函数神经网络 模糊C-均值聚类算法 最优停止法 |
| 收稿时间： | 2006-1-17 |
| 修稿时间： | 2006-01-172006-05-30 |
An Application of Improved RBF Neural Network in Modulation Recognition |
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| YE Jian,GE Lin-Dong,WU Yue-Xian.An Application of Improved RBF Neural Network in Modulation Recognition[J].Acta Automatica Sinica,2007,33(6):652-654. |
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| Authors: | YE Jian GE Lin-Dong WU Yue-Xian |
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| Affiliation: | 1.PLA Information Engineering University, Zhenzhou 450002 |
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| Abstract: | 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 C-means (FCM) 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. |
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| Keywords: | Modulation recognition RBF neural network FCM clustering algorithm optimal stopping rule |
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