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- W2385996857 abstract "The accuracy and reliability of inertial navigation system(INS) is directly influenced by gyro motor's state.The prediction of which is an important way for INS performance evaluation and life prediction.The prediction accuracy of grey theory is limited by the data's randomness.BP neural could solve nonlinear problem and performs well in self-adaption and self organization,but it's training effect and efficiency is limited by the number of samples.In this paper,a hybrid model combining the advantages of grey theory and BP neural network is put forward based on characteristics of gyro motor's state parameters,in which grey model error is modeled by BP neural network,and grey model uses it's output to correct its input.Grey theory,BP neural network and the hybrid model are constructed respectively to model and predict the state parameters.The results indicate that the prediction errors of hybrid model decrease 2/3 in comparison with that of grey model and decreases 1/3 in comparison with that of BP neural network,which prove the validity of the hybrid model." @default.
- W2385996857 created "2016-06-24" @default.
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- W2385996857 date "2010-01-01" @default.
- W2385996857 modified "2023-09-28" @default.
- W2385996857 title "Prediction of gyro motor's state based on grey theory and BP neural network" @default.
- W2385996857 hasPublicationYear "2010" @default.
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