• DocumentCode
    1591307
  • Title

    Currency Recognition Modeling Research Based on BP Neural Network Improved by Gene Algorithm

  • Author

    Cao Bu-Qing ; Liu Jian-xun

  • Author_Institution
    Key Lab. of Knowledge Manage. & Network-based Manuf., Hunan Univ. of Sci. & Technol., Xiangtan, China
  • Volume
    2
  • fYear
    2010
  • Firstpage
    246
  • Lastpage
    250
  • Abstract
    Both artificial neural networks and gene algorithm are a method that people imitates biological treatment pattern and gains the intelligence out of it in order to handle complicated problems. Based on uncertain network model structure and indeterminate initial weights and slow convergence speed for back propagation neural networks, this paper proposes that make use of gene algorithm to improve it in order to find the most suitable network connection weights and network structure, then form GA-BP model and apply it to currency recognition. The experiment indicates that GA-BP model shorten training time for Back Propagation Neural Networks and gain higher recognition speed and better recognition effect, thereby, it is preponderant for image recognition that make use of gene algorithm to improve back propagation neural networks.
  • Keywords
    backpropagation; biology computing; genetic algorithms; neural nets; BP neural network; backpropagation neural networks; biological treatment pattern; currency recognition modeling; gene algorithm; Artificial neural networks; Computer aided manufacturing; Computer networks; Feedforward neural networks; Image recognition; Multi-layer neural network; Neural networks; Pattern recognition; Principal component analysis; Software algorithms; Back Propagation Neural Networks; Currency Recognition; Gene Algorithm; Modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Modeling and Simulation, 2010. ICCMS '10. Second International Conference on
  • Conference_Location
    Sanya, Hainan
  • Print_ISBN
    978-1-4244-5642-0
  • Electronic_ISBN
    978-1-4244-5643-7
  • Type

    conf

  • DOI
    10.1109/ICCMS.2010.270
  • Filename
    5421085