华中科技大学学报（自然科学版）2011，Vol.39Issue(3)：115-119,5.
基于BP神经网络的GFSINS角速度预测
Prediction of the angular velocity of GFSINS by BP neural network
摘要
Abstract
Aimed at low precision for traditional angular velocity algorithms in gyro-free strapdown inertial navigation system (GFSINS), a BP (back-propagation)neural network algorithm without complex mathematic computation was put forward to calculate angular velocity. Based on a ten-accelerometer configuration scheme, the accelerometer output, sample interval and fixed position were chosen as input, angular velocity got by lognormal algorithm was chosen as output, and 5 000 samples were trained in several conditions with different hiding layers, neural cells and training steps. Then a threelayer BP network model with 30 hiding layer neural cells was built. Finally, the angular velocity was predicted in real time by the model. Results demonstrate that network has strong adaptive capability and instantaneity, and compared with lognormal algorithm, prediction time is almost the same, but the prediction precision of angular velocity is nearly improved by 3 times.关键词
无陀螺捷联惯导系统/角速度预测/反向传播神经网络/对数法/十加速度计Key words
gyro-free strapdown inertial navigation system (GFSINS)/ angular velocity prediction/BP (back-propagation) neural network/ lognormal algorithm/ ten-accelerometer分类
交通工程引用本文复制引用
韩庆楠,郝燕玲,刘志平,王瑞..基于BP神经网络的GFSINS角速度预测[J].华中科技大学学报（自然科学版）,2011,39(3):115-119,5.基金项目
国家自然科学基金资助项目(60604019). （60604019）
