Research on Temperature Compensation of Quartz Vibrating Beam Accelerometer Based on IWOA-BP
The output of a quartz vibrating beam accelerometer(QVBA)drifts in an environment with varying temperatures.In order to improve the temperature stability of QVBA,a temperature compensation model based on the improved whale optimiza-tion algorithm(IWOA)to optimize the BP neural network was proposed.By optimizing the initial weights and thresholds of the BP neural network,the IWOA overcame the shortcoming that the BP neural network was easy to fall into local optimum,and en-hanced the accuracy and robustness of the BP neural network in training.Full-temperature experiments show that this method can significantly suppress the temperature-induced drift of QVBA.After compensation,the bias stability decreased from 4.161 mg to 0.196 mg,and the scale factor stability decreased from 59.676 ppm to 35.751 ppm,which verified the validity of the model.
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