摘要
提出了应用神经网络对由单特征值组成的高阶特征量进行综合评判 ,以提取的信号特征值作为神经网络的输入值 ,构造了地下核爆炸和天然地震的隶属度函数 ,根据隶属度的大小 ,确定神经网络的输出结果。并针对在核爆炸模式识别中 BP网络收敛速度较慢的缺点 ,采用改进的 delta算法。结果表明 ,方法是可行的 ,改进后的网络训练时间比 BP网络明显缩短 ,识别率比单特征值有很大的提高 ,达到 92 %。
Many features are extracted to improve the identified rate and reliability of underground nuclear explosion and natural earthquake. But how to synthesize these characters is the key of pattern recognition .In this paper, based on the improved Delta algorithm, features of underground nuclear explosion and natural earthquake are inputted into BP neural network, and friendship functions are constructed to identify the output values. The identified rate is up to 92.0%,which shows that: the way is feasible.
出处
《核电子学与探测技术》
CAS
CSCD
北大核心
2000年第4期279-283,共5页
Nuclear Electronics & Detection Technology
关键词
地下核爆炸
天然地震
神经网络
综合评判
neural network
underground nuclear explosion
natural e arthquake
comprehensive judgement
