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- W2358909918 abstract "The zero bias of MEMS gyroscope exhibits nonlinear change with varying temperature and contains significant stochastic error.In view that traditional polynomial method could not accurately establish the relationship between the temperature and the zero bias,this paper puts forward a hybrid model based on grey model theory and RBF neural network.First,it pre-processes the gyro output using the grey model to reduce the noise.Then it uses the processed sample data to train the RBF network,so the training errors are reduced by one magnitude within the same training times.The verification experiments indicate that compared with traditional polynomial model and untreated RBF neural network,the compensated gyro zero bias error is reduced by one order of magnitude and 2/3 respectively." @default.
- W2358909918 created "2016-06-24" @default.
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- W2358909918 date "2010-01-01" @default.
- W2358909918 modified "2023-09-22" @default.
- W2358909918 title "Temperature compensation of MEMS gyroscope based on grey model and RBF neural network" @default.
- W2358909918 hasPublicationYear "2010" @default.
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