计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 196-200.doi: 10.11896/jsjkx.191200142
张英1,2,3, 陶磊岩4, 曹健1, 王世会2,3, 赵茜2,3, 张兴1
ZHANG Ying1,2,3, TAO Lei-yan4, CAO Jian1, WANG Shi-hui2,3, ZHAO Qian2,3, ZHANG Xing1
摘要: 为了满足飞行器实时飞行过程中对大量异构输入数据的信息处理需求,文中提出了一种神经网络,其包括卷积定点滑动核、池化压缩量化核以及全连接压缩融合核,将飞行器异构传感器多路并行数据作为系统的输入,将辨识结果作为系统的输出。卷积滑动窗口核通过排除冗余数据的滑动窗快速实现数据特征的提取;池化压缩量化核使用压缩量化技术来提高系统的执行效率;全连接压缩融合核经删减量化后压缩融合并输出。该设计满足了飞行器对高可靠性、低功耗的在线智能集成需求。使用所提压缩量化方法,准确率最高可达98.54%,压缩率为77.8%,运行速度提升了40倍。
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