• DocumentCode
    3266586
  • Title

    Research on the Genetic Neural Network for the Computation of Ship Resistance

  • Author

    Ai-Guo, Chen ; Jia-wei, Ye

  • Author_Institution
    Sch. of Civil Eng. & Transp., South China Univ. of Technol., Guangzhou, China
  • Volume
    1
  • fYear
    2009
  • fDate
    6-7 June 2009
  • Firstpage
    366
  • Lastpage
    369
  • Abstract
    The topology structure of the neural network for the computation of ship resistance is designed. The evaluation function adopts msereg. Applying the original experimental data of series 60 ship models, the overall arithmetical crossover and the adaptive mutation, optimize the weights and threshold values of the neural network by genetic algorithm. Then, applying back propagation algorithm to go on training the neural network, develop the optimal genetic neural network for the computation of ship resistance. Easily and quickly calculating ship resistance, the neural network can be applied to research the performance of ship resistance, the optimization of hull form and the optimal matching design of ship engine and propeller.
  • Keywords
    backpropagation; engines; genetic algorithms; neural nets; product design; propellers; ships; adaptive mutation; arithmetical crossover; back propagation; genetic algorithm; msereg; optimal genetic neural network; optimal matching design; ship engine; ship model; ship propeller; ship resistance; threshold value; topology structure; weights value; Computer networks; Design optimization; Engines; Genetic algorithms; Genetic mutations; Marine vehicles; Network topology; Neural networks; Optimal matching; Propulsion; genetic algorithm; neural network; series 60; ship resistance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3645-3
  • Type

    conf

  • DOI
    10.1109/CINC.2009.34
  • Filename
    5231126