中文摘要：

针对微机电系统（MEMS）加速度计零位漂移大的问题,研究了一种基于Birgé-Massert（BM）阈值小波包降噪的广义回归神经网络对MEMS加速度计零位漂移进行非线性抑制的新方法。该方法首先利用BM阈值小波包滤除零位漂移中的噪声,然后利用广义回归网络对非线性数据的无限逼近原理,来建立MEMS加速度计的零漂模型。将实测数据代入模型,计算结果表明,经过该模型补偿后的零漂输出结果同未经补偿、最小二乘拟合补偿、未经滤波建模补偿相比,均值分别减小97.4%、67.8%、67.8%,均方差分别减小87.4%、87.5%、90.9%;利用训练后的模型进行实时补偿延迟时间为10^-5 s。分析结果证明了基于BM阈值小波包降噪滤波技术的广义回归网络组合模型的有效性和合理性。

英文摘要：

The static time drift of MEMS accelerometer was studied through theory and experiments,and a BM wavelet packets general regression neural network（GRNN）was developed to compensate the drift.The first,the error of the drift was removed by the wavelet packet based on the Birgé-Massert improved function,the second,the zero drift model of the MEMS accelerometer was established based on GRNN,which has a good approximation capability,fast learning speed and excellent network stability.The computer results show that this model can compensate the zero drift effectively.Comparing with the original data,compensation with least square fitting and compensation only by GRNN,the mean values of zero drift is reduces by 97.4%,67.8%,67.8% respectively;the variance reduces by 87.4%,87.5%,90.9%,respectively.The delay time of the model is 10^-5s.The results illustrate the feasibility and validity of the Birgé-Massert wavelet packers general regression neural network model.