摘要
基于小波变换(WT)的多尺度分析能力和径向基函数(RBF)神经网络良好的非线性预测与集成能力,研究了一种非线性集成预测方法。针对贮存期石英挠性加速度计零偏漂移抑制的问题,提出了基于WT和RBF神经网络的一种石英挠性加速度计零偏非线性集成预测方法。为验证所提方法的有效性,设计了一种加速度计参数的重力场标定实验,并针对某型号石英挠性加速度计进行了为期2年的标定实验。分别利用所提WT—RBF集成模型和RBF模型对零偏标定序列进行了预测分析,仿真结果显示:WT—RBF集成模型具有更好的预测性能。
Based on multi-scale analysis capabilities of wavelet transform and good nonlinear prediction and integrated capabilities of RBF neural network,a nonlinear integrate prediction method is studied. Aiming at problem of zero-bias drift inhibition during storage,of quartz elastic accelerometer,a zero-bias nonlinear integeration predictive method for accelerometer,which based on WT and RBF,is proposed. In order to verify the effectiveness of the proposed method,a gravitational field calibration test of accelerometer parameters is designed.The two-year calibration experiment of some type quartz accelerometer is carried out. Prediction analysis on zerobias calibration sequence by WT—RBF integrate model and RBF model,simulation results show that the WT—RBF integrate model,compared with the single RBF model,has better prediction performance.
出处
《传感器与微系统》
CSCD
2016年第4期11-14,共4页
Transducer and Microsystem Technologies
关键词
小波变换
径向基函数
集成预测
加速度计零偏漂移
wavelet transform（WT）
radial basis function（RBF）
integrate prediction
accelerometer zero-bias drift
作者简介
陈雪东（1967-），男，湖南邵阳人，副研究员，主要研究领域为传感器测试与信号分析处理。
