• DocumentCode
    3096312
  • Title

    Marine traffic accident prediction based on particle swarm optimization-based RBF neural network

  • Author

    Yang Zhen-Qi

  • Author_Institution
    Shandong Jiaotong Univ., Jinan, China
  • Volume
    1
  • fYear
    2011
  • fDate
    11-13 March 2011
  • Firstpage
    435
  • Lastpage
    438
  • Abstract
    The future marine traffic accident situation is shown by using the marine traffic accident prediction method. Thus, marine traffic accident prediction method based on particle swarm optimization-based RBF neural network is presented in the paper. Particle swarm optimization algorithm, a kind of population-based optimization algorithm, is used to adjust the connection weights and the center and width of radial basis function. The marine traffic accidents of a certain terminal from 1996 to 2007 are applied to study the feasibility of the proposed PSO-RBF neural network. The comparison results between the proposed PSO-RBF neural network and normal RBF neural network can indicate that the prediction results of marine traffic accidents of the proposed PSO-RBF neural network are better than those of RBF neural network.
  • Keywords
    marine accidents; particle swarm optimisation; radial basis function networks; traffic engineering computing; PSO-RBF neural network; marine traffic accident prediction method; particle swarm optimization; population-based optimization algorithm; radial basis function neural network; Accidents; Approximation algorithms; Artificial neural networks; Optimization; Particle swarm optimization; Prediction algorithms; Prediction methods; marine traffic accident; neural network; particle swarm optimization; prediction model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Research and Development (ICCRD), 2011 3rd International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-61284-839-6
  • Type

    conf

  • DOI
    10.1109/ICCRD.2011.5764052
  • Filename
    5764052