摘要
为了克服传统目标识别方法的缺点,建立了一种基于粗集理论和神经网络结合的粗集-神经网络模型.利用粗集对输入信息进行约简,剔除冗余信息,简化了生成规则和神经网络模型结构,提高了网络训练速度和运行速度,并通过仿真实验证明了该混合模型的可行性.
A network model based on rough set theory and neural network is built to surmount the shortcomings of traditional target identification means. Rough set is used to predigest the input information, eliminate the redundant information, reduce the rules and the structure of neural network model and improve the speed of training and running. The simulation result proves the feasibility of this mixed model.
出处
《战术导弹技术》
北大核心
2006年第4期47-50,共4页
Tactical Missile Technology
关键词
粗集
神经网络
目标识别
rough set
neural network
target identification
作者简介
赵双杰 硕士研究生。
