• DocumentCode
    3096293
  • Title

    A new method of multi-sensor information fusion based on SVM

  • Author

    Li, Zhi-xin ; Ma, Yong-guang

  • Author_Institution
    Dept. of Control Sci. & Eng., North China Electr. Power Univ., Baoding, China
  • Volume
    2
  • fYear
    2009
  • fDate
    12-15 July 2009
  • Firstpage
    925
  • Lastpage
    929
  • Abstract
    The support vector machine (SVM) is an algorithm based on structure risk minimizing principle, having high generalization ability. In the course of multi-sensor information fusion of industrial control, sensor has bigger nonlinearity and fuzzy relation between coefficient and relevant parameter. A kind of model and algorithm of multiple sensor information fusion based on the support vector machine are proposed. The model offered a kind of effective way for little sample, non-linear, high dimension.
  • Keywords
    regression analysis; sensor fusion; support vector machines; SVM; industrial control; multisensor information fusion; structure risk minimizing principle; support vector machine; Cybernetics; Industrial control; Information processing; Machine learning; Machine learning algorithms; Redundancy; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Support vector machines; Information fusion; Regression function; SVM; Sample; Sensor;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2009 International Conference on
  • Conference_Location
    Baoding
  • Print_ISBN
    978-1-4244-3702-3
  • Electronic_ISBN
    978-1-4244-3703-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2009.5212442
  • Filename
    5212442