• DocumentCode
    412621
  • Title

    Truck backing up neural network controller optimized by genetic algorithms

  • Author

    Ho, M.L. ; Chan, P.T. ; Rad, A.B. ; Mak, C.H.

  • Author_Institution
    Dept. of Electr. Eng., Hong Kong Polytech. Univ., China
  • Volume
    2
  • fYear
    2003
  • fDate
    8-12 Dec. 2003
  • Firstpage
    944
  • Abstract
    Jenkins-Yuhas network has been applied successfully to solve the trailer-truck backing up problem. Genetic algorithms (GA) is used to train the Jenkins-Yuhas network controller. The integration of genetic algorithms and neural networks training avoids complicated formulation of derivative function that required in conventional error back propagation techniques (gradient descent). In addition, it can avoid trapping in local minimum point. The performance of GA trained neural network controller is evaluated via simulation studies. It has been demonstrated that the controller can successfully control trailer-truck for different initial parking conditions, i.e. with same set of trained weights, to the loading dock.
  • Keywords
    backpropagation; genetic algorithms; neurocontrollers; GA; Jenkins-Yuhas network controller; derivative function; error back propagation technique; genetic algorithm; loading dock; neural networks training; optimization; parking conditions; trailer-truck backing up problem; Control systems; Genetic algorithms; Mathematical model; Multi-layer neural network; Neural networks; Neurons; Nonlinear control systems; Optimization methods; System analysis and design; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
  • Print_ISBN
    0-7803-7804-0
  • Type

    conf

  • DOI
    10.1109/CEC.2003.1299768
  • Filename
    1299768