This paper mainly proposes an algorithm that the speedy constringency classifier of neural networks and proposes a system model of online modulation recognition.
提出了一种在线进行调制识别的系统模型，并给出一种基于神经网络的快速收敛分类器算法。
Techniques such as Bayesian networks or neural networks use highly expressive models, which try to produce a non-biased classifier in order to "describe" a corpus of documents.
Bayesian网络或神经网络等技术使用表达能力非常强的模型，力求生成无偏向的分类器来“描述”文档集。
The forth-order and sixth-order cumulants of received signal are adopted for features extraction while RBF neural networks with a new designed training cost function being used for classifier.
采用信号四阶和六阶统计量提取信号特征，使用新设计的误差函数训练RBF神经网络，使得识别的效率和正确度得到了明显的改善。
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