• DocumentCode
    3389575
  • Title

    Shape of object recognition based on RS-ANN for mobile robot

  • Author

    Shuang Liu ; Jie Dong ; Xin Xing

  • Author_Institution
    Dept. of Electr. Eng., Jilin Technol. Coll. of Electron. Inf., Jilin, China
  • fYear
    2011
  • fDate
    19-22 Aug. 2011
  • Firstpage
    193
  • Lastpage
    196
  • Abstract
    When studying mobile robot to recognize shape of object in dynamic surroundings, we proposed a hybrid recognition algorithm based on the combination of rough set theory and BP neural network. RS has the capability for intelligent data analysis, and BP network can approach most problems accurately and exactly, the algorithm put respective advantages of two theories to use. Firstly, information table which was formed by training sample set was reduced by RS in order to find minimal decision regulations, and then the regulations confirmed the structure of ANN and recognized the shape by BP neural network. At the same time, the reduction of RS enhanced the efficiency of training sample set, and simplified the scale of neural network. Experimental results showed that the algorithm here had the better performance in exactness and speediness when compared with the only BP network.
  • Keywords
    backpropagation; mobile robots; neural nets; object recognition; rough set theory; BP neural network; RS-ANN; dynamic surroundings; hybrid recognition algorithm; intelligent data analysis; mobile robot; object recognition; rough set theory; Artificial neural networks; Biological neural networks; Mobile robots; Partitioning algorithms; Shape; Target recognition; Training; BP networks; mobile robot; pattern recognition; rough set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
  • Conference_Location
    Jilin
  • Print_ISBN
    978-1-61284-719-1
  • Type

    conf

  • DOI
    10.1109/MEC.2011.6025433
  • Filename
    6025433