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论文摘要

基于改进的自组织映射神经网络的 调制方式识别分类器

Classifier of Modulation Recognition Based onModified Self-organizing Feature Map Neural Network

作者:高玉龙(哈尔滨工业大学 通信技术研究所,黑龙江 哈尔滨 150001);张中兆(哈尔滨工业大学 通信技术研究所,黑龙江 哈尔滨 150001)

Author:(Communication Research Center, Harbin Inst. of Technol., Harbin 150001, China);(Communication Research Center, Harbin Inst. of Technol., Harbin 150001, China)

收稿日期:2005-12-19          年卷(期)页码:2006,38(5):143-147

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:自组织映射神经网络;调制方式识别;学习规则;神经元节点;竞争传递函数

Key words:self-organizing feature map neural network; modulation recognition; learning rule; neuron node; competitive transferring function

基金项目:国家863计划资助项目(2004AA001210)

中文摘要

为了提高调制方式识别分类器算法的正确识别概率和缩短识别时间,使其有自适应能力,利用自组织映射神经网络自组织、自适应的特点,提出采用自组织映射神经网络作为调制方式中的分类器,以自适应于信噪比的变化。对其学习规则和竞争传递函数进行改进,使每次获胜的输出神经元为2个。这样能减少输出神经元个数,加快神经网络的收敛速率,以较短的时间识别接收信号的调制方式。仿真结果表明改进的自组织映射神经网络的识别概率高于其它的神经网络。并且由于其结构简单,便于工程实现。

英文摘要

In order to improve recognition probability and decrease recognition time of the classifier of modulation recognition,and to make it adaptive, the self-organizing feature map neural network(SOM) was used as the classifier of modulation recognition, applying its property of self organizing and adaptation to adapt automatically variety of the signal to noise ratio .Learning rule and competitive transferring function were modified in order to have two victorious output neurons. The number of output neurons could be decreased and the convergent rate of neural network could be improved thought those ameliorations, and modulation type could be recognized in less time. The simulation results proved that recognition probability of modified SOM is higher than other neural networks, and modified classifier is implemented easily in practical engineering because of its simple structure.

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