Advanced Search
Volume 39 Issue 11
Nov.  2013
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2013  >  39(11): 1558-1562.
Xu Zhe, Liu Yunfeng, Dong Jingxinet al. Thermal bias drift compensation of MEMS accelerometer based on relevance vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(11): 1558-1562. (in Chinese)
Citation: Xu Zhe, Liu Yunfeng, Dong Jingxinet al. Thermal bias drift compensation of MEMS accelerometer based on relevance vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(11): 1558-1562. (in Chinese)
shu

Thermal bias drift compensation of MEMS accelerometer based on relevance vector machine

  • Department of Precision Instrument, Tsinghua University, Beijing 100084, China

  • Received Date: 18 Dec 2012
  • Publish Date: 30 Nov 2013
    Abstract
  • Thermal bias drift prognosis and compensation model was built based on the regression algorithm of relevance vector machine. The thermal bias drift of the accelerometer experiencing different temperature load can be classified by using both the temperature and the temperature rate as the model input. The influence of training sample number, the kernel function and the parameter sigma were discussed. Experimentation with the data of the temperature cycling test was conducted. According to the experimental result, the thermal bias drift of the accelerometer can be prognosed accurately by the model, the mean square error is less than 1%, and the size of the thermal hysteresis loop is reduced from 0.06g to 0.015g.

     

    • FullText(HTML)
  • loading
    • References(12)
  • [1]
    董景新.惯性仪表——微机械加速度计[M].北京:清华大学出版社, 2002:1-5 Dong Jingxin.Micro inertial instrument:micromechanical accelerometer[M].Beijing:Tsinghua University Press, 2002:1-5 (in Chinese)
    [2]
    张鹏飞, 王宇, 龙兴武, 等.加速度计温度补偿模型的研究[J].传感技术学报, 2007, 20 (5):1012-1016 Zhang Pengfei, Wang Yu, Long Xingwu, et al.Research on temperature compensating model for accelerometer[J].Chinese Journal of Sensors and Actrator, 2007, 20 (5):1012-1016 (in Chinese)
    [3]
    王立昆, 杨新锋.一种基于RVM回归的分类方法[J].电子科技, 2011, 24 (5):14-16 Wang Likun, Yang Xinfeng.A classification method based on RVM regression[J].Electronic Science and Technology, 2011, 24 (5):14-16 (in Chinese)
    [4]
    陈佳, 颜学峰, 钟伟民, 等.基于多项式核rvm的非线性模型预测控制[J].控制工程, 2008, 15 (2):158-160 Chen Jia, Yan Xuefeng, Zhong Weimin, et al.Nonlinear model predictive control based on RVM with polynomial kernel[J].Control Engineering of China, 2008, 15 (2):158-160 (in Chinese)
    [5]
    Tipping M E.Sparse Bayesian learning and the vector machine[J].The Journal of Machine Learning Research, 2001, 1 (3):211-244
    [6]
    Faul A C, Tipping M E.Analysis of sparse Bayesian learning[C]//Advances in Neural Information Processing Systems (NIPS 14) .Vancouver:NIPS, 2002:383-389
    [7]
    Wong P K, Xu Q, Vong C M, et al.Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine[J].Industrial Electronics, IEEE Transactions on, 2012, 59 (4):1988-2001
    [8]
    Ding Errui, Zeng Ping, Yao Yong.A novel regressive algorithm based on relevance vector machine[C]//Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) .Piscataway, NJ:IEEE, 2007:463-467
    [9]
    Wong P K, Wong H C, Vong C M.Modelling and prediction of automotive engine airratio using relevance vector machine[C]//2012 12th International Conference on Control Automation Robotics & Vision (ICARCV) .Piscataway, NJ:IEEE, 2012:1710-1715
    [10]
    Liu F, Song H, Qi Q, et al.Time series regression based on relevance vector learning mechanism[C]//2008 International Conference on Wireless Communications, Networking and Mobile Computing.Piscataway, NJ:IEEE Computer Society, 2008:1-4
    [11]
    Yang B, Zhang Z, Sun Z.Robust relevance vector regression with trimmed likelihood function[J].Signal Processing Letters, IEEE, 2007, 14 (10):746-749
    [12]
    Yuan J, Bo L, Wang K, et al.Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression[J].Expert Systems with Applications, 2009, 36 (2):3982-3989
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(1608) PDF downloads(600) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return