调制样式BP神经网络分类器

Classifier of Modulation Types Using BP Neural Network

给出从信号幅度、相位、频率及功率谱等特性中提取的五种特征参数的定义 ,详细研究了应用这 5种参数识别CW、AM、SSB、FM、2FSK、2PSK、NOISE信号的BP神经网络分类器。该分类器采用Levenberg arquardt神经网络学习算法 ,隐含层采用log sigmoid函数为激励函数 ,输出层采用tan sigmoid函数为激励函数 ,隐含层的神经元个数取 10个。

Five characteristic parameters derived from the instantaneous amplitude, instantaneous phase, instantaneous frequency and power spectrum of modulated signal are presented in this article. BP neural network classifier is studied to identify modulation including CW、AM、SSB、FM、2FSK、2PSKand NOISE with these parameters. This classifier uses Levenberg-Marquardt algorithm and ten nodes of the hidden layer. It is found that when the log-sigmoid function is used as the excitng function in the hidden layer and the tan-sigmoid function as the exciting function in the output layer, its recognition performance is better. Computer simulation results are given.