用HMM框架下的神经网络分类器识别雷达目标  

RADAR TARGET IDENTIFICATION USING NEURAL NETWORK CLASSIFIER BASED ON HMM FRAMEWORK

作　　者：周德全[1] 郭耀红[1] 

机构地区：[1]空军第一航空学院科研部,河南信阳464000

出　　处：《红外与毫米波学报》2001年第2期107-110,共4页Journal of Infrared and Millimeter Waves

摘　　要：提出了一种 HMM框架下的神经网络分类器 ,它既克服了普通神经网络不能有效地识别时变信号的缺点 ,又解决了 HMM识别时变信号时不能突出不同信号的差异性问题 .用网络权的遗传算法进化学习解决了 Baum-Welch及 BP网络学习中易陷入局部极小点的问题 .A neural network classifier based on HMM framework was proposed.It can be used to identify timevarying signal, which an ordinary neural network lacks of, and can stress the differences of different signals. Evolutionary learning of the neural network weights using genetic algorithm solves the problem of falling into local minimum point which BP and Baum Welch algorithmnace. An example of recognizing radar return signal successfully by the neural netwrok was presented.

关 键 词：隐含Markov模型 遗传算法 目标识别 神经网络分类器 雷达目标 HMM 

分 类 号：TN959.17[电子电信—信号与信息处理] TP183[电子电信—信息与通信工程]