基于新型RBF网络的模拟调制信号识别

对敌方雷达信号调制类型的识别是电子对抗的一个重要方面。采用了一种基于人工免疫聚类和进化规划的混合算法设计径向基函数(RBF)网络，并将其应用于雷达信号调制类型的自动识别。该算法首先利用一种实现数据聚类的人工免疫机制，根据输入数据集合自适应地确定RBF网络初始中心的数量和位置，之后采用进化规划训练RBF网络。仿真实验表明，采用这种方法设计的RBF网络对各种模拟调制信号的调制类型达到了较高的识别精度。

Recognition of analog-modulated signals based on a novel RBF network

Modulation-style recognition of radar signals is an important aspect in electronic warfare. A hybrid RBF training algorithm combined with artificial immune clustering and evolutionary programming is proposed and applied in modulation-style recognition of radar signals. In the algorithm, artificial immunology for data clustering was used to adaptively determine the amount and the positions of initial RBF centers according to input data set. Then the RBF network was trained with evolutionary programming. Computer simulations demonstrate that the recognition rate of different analog modulation styles designed by the RBF network in this method is high.