基于神经网络的多尺度多参数硅微陀螺仪研究（英文）

A Novel Method of Neural Network Based on Multi-Scale Multi-Parameter for Silicon MEMS Gyroscope

由于硅微陀螺仪工艺加工导致漂移误差进而影响INS测量精度,所以硅微陀螺仪的漂移估计成为研究的重点。硅微陀螺仪精度受多种因素的影响,因此很难对非平稳非线性输出硅微陀建立准确的误差模型。提出一种的基于小波神经网络的多尺度和多参数非线性估计改进硅微陀螺仪的漂移估计。实验结果表明,在通过本文介绍基于神经网络的多尺度多参数校准后硅微陀螺仪精度从1°/s到0.05°/s。

The silicon MEMS-Gyro’s drift affect precision of INS because of machining technology. So estimated the drift of silicon MEMS-Gyro became the focus of research. It was difficult to build the error model of silicon MEMS-Gyro accurately,Because of the precision of silicon MEMS-Gyro was affected by a lot of factors,and arming at the output of non-stationary nonlinear characteristics. In the thesis,stationary multi-scale and multi-parameter nonlinear estimated the drift of silicon MEMS-Gyro was proposed base on the improved wavelet neural network method. The experimental results show that the precision of the MEMS-Gyro has been improved from 1 °/s to 0.05 °/s by the method of multi-scale and multi-parameter.