摘要
提出一种新的飞机自动识别算法。采用矩不变量和傅立叶描述子相结合的表示方法简单而有效地描述飞机的特征；采用了分布存储的ＢＰ神经网络来减少存储量和加速搜索识别过程，并对ＢＰ网络的收敛方法提出了若干措施加速其收敛过程。通过对９架不同飞机的大量实验证实，本系统性能优越，其识别准确率在９９％以上，并且识别速度很快，一架飞机的整个识别过程在不到１ｓ的时间内就可完成。
A new approach is proposed to recognize aircraft automatically. To describe the characters of aircraft simply and efficiently,moment invariants and Fourier descriptors are combined. A neural network with distributive storage ability is used. At the same time, several methods are taken to accelerate its convergent process and experiments of nine different aircraft have proved that this recognition system performs well; it has an accuracy over 99% and high recognition speed. The whole recognition process can be completed within 1 second.
出处
《航空学报》
EI
CAS
CSCD
北大核心
1998年第6期730-735,共6页
Acta Aeronautica et Astronautica Sinica
基金
航空科学基金
关键词
矩不变量
傅立叶描述子
飞机
自动识别
moment invariant, Fourier descriptor, BP neural networks
