In the space gravitational wave detection mission,high-precision inertial sensors must undergo ground testing and evaluation before being placed in orbit.The torsion balance is the preferred device for ground testing.In order to obtain an accurate dynamical model of the torsion balance,to reduce its inherent systematic error,a torsion balance dynamic prediction model based on reservoir computing(RC)is proposed.The proposed model utilizes attention mechanisms to enhance the long-term dependence features of time-series data,and employs Bayesian optimization algorithms to locate the optimal hyperparameter space,thereby improving the predictive accuracy of the dynamical model.The effectiveness of the proposed method is validated on time-series data of torsion balance angular displacement collected in the laboratory.Compared with the traditional RC methods,the prediction error of the proposed method is reduced by more than 40%on average,which can provide a reliable dynamic reference for ground testing of inertial sensors.
inertial sensortorsion balancedata processingreservoir computingtime series forecasting
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