• DocumentCode
    571569
  • Title

    The Research of Alphabet Identification Based on Genetic BP Neural Network

  • Author

    Liu, Lina ; Qi, Huijuan ; Liu, Jia

  • Author_Institution
    Shijiazhuang Inst. of Railway Technol., Shijiazhuang, China
  • Volume
    1
  • fYear
    2012
  • fDate
    26-27 Aug. 2012
  • Firstpage
    25
  • Lastpage
    28
  • Abstract
    The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.
  • Keywords
    backpropagation; character recognition; genetic algorithms; neural nets; BP training algorithm; MATLAB simulation; add-noise alphabet; alphabet identification; back propagation neural network genetic algorithm; genetic BP neural network; genetic learning algorithm; global optimization; letter recognition algorithm; network error recognition rate; neural network weights; recognition speed; Algorithm design and analysis; Biological neural networks; Genetic algorithms; Genetics; Noise; Training; BP neural network; additive noise; alphabet identification; genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
  • Conference_Location
    Nanchang, Jiangxi
  • Print_ISBN
    978-1-4673-1902-7
  • Type

    conf

  • DOI
    10.1109/IHMSC.2012.12
  • Filename
    6305616