• DocumentCode
    3188340
  • Title

    Research of standard handwritten English letters recognition system based on the PSO-BP neural network

  • Author

    Xu, Peng

  • Author_Institution
    Fac. of Technol., Harbin Univ., Harbin, China
  • fYear
    2011
  • fDate
    8-10 Aug. 2011
  • Firstpage
    3234
  • Lastpage
    3237
  • Abstract
    The Particle swarm optimizer is intelligent searching in space to find the optimal solutions through the cooperation and competition between particles, being based on the theory of swarm intelligence global optimization algorithm. Its advantage is that simple operation and easy achievement. In this paper, a new algorithm PSO- BP was studied, giving full play to both of the particle swarm algorithm of global optimization ability and BP algorithm´s local search advantage, and compared identification of 140 pixels of English letters together with BP algorithm. Experimental results show that particle swarm algorithm used for the optimization of the neural network has a faster convergence speed, and simpler algorithm.
  • Keywords
    backpropagation; convergence; handwriting recognition; natural language processing; neural nets; particle swarm optimisation; search problems; BP algorithm; PSO-BP neural network; convergence speed; global optimization ability; intelligent searching; local search advantage; optimal solutions; particle swarm algorithm; particle swarm optimizer; standard handwritten English letters recognition system; swarm intelligence global optimization algorithm; Algorithm design and analysis; Biological neural networks; Convergence; Feature extraction; Optimization; Particle swarm optimization; Training; Artificial neural network; English letters recognition; Particle swarm algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
  • Conference_Location
    Deng Leng
  • Print_ISBN
    978-1-4577-0535-9
  • Type

    conf

  • DOI
    10.1109/AIMSEC.2011.6011353
  • Filename
    6011353