Advanced Search
Volume 40 Issue 1
Jan.  2014
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2014  >  40(1): 125-130.
Zhao Changchun, Jiang Xiaoai, Jin Yinghanet al. Improved SMO algorithm of nonlinear regression support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(1): 125-130. (in Chinese)
Citation: Zhao Changchun, Jiang Xiaoai, Jin Yinghanet al. Improved SMO algorithm of nonlinear regression support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(1): 125-130. (in Chinese)
shu

Improved SMO algorithm of nonlinear regression support vector machine

  • 1.

    School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China

  • Received Date: 28 Jan 2013
  • Publish Date: 20 Jan 2014
    Abstract
  • In order to solve the regression problems of nonlinear data and nonlinear function, the support vector machine (SVM) sequential minimal optimization (SMO) algorithm was adopted. The original SMO algorithm has deficiencies such as low training speed and instability training results. To accelerate the training process of SMO algorithm and promote training stability of the solution, the SMO algorithm was improved by updating the optimization multipliers method, using double threshold values, caching kernel function outputs, adding stop criterion. Simulation results show that the improved algorithm performs well for regression of nonlinear data and nonlinear function, and it has faster training speed and better training result stability than original SMO algorithm.

     

    • FullText(HTML)
  • loading
    • References(10)
  • [1]
    Platt J C.Fast training of support vector machines using sequential minimal optimization[R].MSR-TR-98-14, 1998
    [2]
    Smola A J, Scholkopf B.A tutorial on support vector regression[R].NC2-TR-1998-030, 1998
    [3]
    Shevade S K, Keerthi S S, Bhattacharyya C, et al.Improvements to SMO algorithm for SVM regression[J].IEEE Transactions on Neural Networks, 2000, 11(5):1188-1193
    [4]
    Flake G W, Lawrence S．Efficient SVM regression training with SMO[J].Machine Learning, 2002, 46(1-3):271-290
    [5]
    张浩然, 韩正之.回归支持向量机的改进序列最小优化学习算法[J].软件学报, 2003, 14(12):2006-2013 Zhang Haoran, Han Zhengzhi.An improved sequential minimal optimization learning algorithm for regression support vector machine[J].Journal of Software, 2003, 14(12):2006-2013 (in Chinese)
    [6]
    刘胜, 李妍妍.自适应GA-SVM参数选择算法研究[J].哈尔滨工程大学学报, 2007, 28(4):398-402 Liu Sheng, Li Yanyan.Parameter selection algorithm for support Vector machines based on adaptive genetic algorithm[J].Journal of Harbin Engineering University, 2007, 28(4):398-402 (in Chinese)
    [7]
    闫国华, 朱永生.支持向量机回归的参数选择方法[J].计算机工程, 2009, 35(14):218-220 Yan Guohua, Zhu Yongsheng.Parameters selection method for support vector machine regression[J].Computer Engineering, 2009, 35(14):218-220 (in Chinese)
    [8]
    董磊, 任章, 李清东.基于SMO-SVR的飞机舵面损伤故障趋势预测[J].北京航空航天大学学报, 2012, 38(10): 1300-1305 Dong Lei, Ren Zhang, Li Qingdong.Fault prediction for aircraft control surface damage based on SMO-SVR[J].Journal of Beijing University of Aeronautics and Astronautics, 2012, 38(10):1300-1305 (in Chinese)
    [9]
    王书舟, 伞冶, 张允昌.基于支持向量机改进 SMO 算法的直升机旋翼自转着陆过程建模[J].航空学报, 2009, 30(1): 46-51 Wang Shuzhou, San Ye, Zhang Yunchang.Modeling for landing process of helicopter with rotator self-rotating based on modified SMO algorithm of support vector machine[J].Acta Aeronautica et Astronautica Sinica, 2009, 30(1):46-51(in Chinese)
    [10]
    王定成.支持向量机建模预测与控制[M].北京:气象出版社, 2009:48-49 Wang Dingcheng.Prediction and control based on support vector machine modelling[M].Beijing:Meteorological Press, 2009:48-49(in Chinese)
    • 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(1842) PDF downloads(583) 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