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Application of RBF Neural Network in Transfer Alignment of INS

机译:RBF神经网络在惯导系统传递对准中的应用

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摘要

Considering the character of Kalman filter’s bad real-time qulity when system step is high, RBF neural network is applied to transfer alignment for carrier aircraft’s INS. Through training of the sample of the input and output of Kalman filter, the output of neural network could be got, and realize the function of estimation of transfer alignment of INS were realized. Simulation results show that with the application of RBF neural network in transfer alignment can not only get the precision similar to Kalman filter, but also reduce the calculating time of system efficiently and raise the real-time quality.
机译:考虑到系统步长较高时Kalman滤波器实时质量差的特点,RBF神经网络被应用于舰载INS的传递对准。通过训练卡尔曼滤波器的输入和输出样本,可以获得神经网络的输出,并实现了惯导传递对准估计的功能。仿真结果表明,RBF神经网络在传递对准中的应用不仅可以获得与卡尔曼滤波器相似的精度,而且可以有效地减少系统的计算时间,提高实时性。

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