• DocumentCode
    562796
  • Title

    Artificial neural network based prediction of optimal pseudo-damping and meta-damping in oscillatory fractional order dynamical systems

  • Author

    Das, Saptarshi ; Pan, Indranil ; Sur, Khrist ; Das, Shantanu

  • Author_Institution
    Dept. of Power Eng., Jadavpur Univ., Kolkata, India
  • fYear
    2012
  • fDate
    30-31 March 2012
  • Firstpage
    350
  • Lastpage
    356
  • Abstract
    This paper investigates typical behaviors like damped oscillations in fractional order (FO) dynamical systems. Such response occurs due to the presence of, what is conceived as, pseudo-damping and meta-damping in some special class of FO systems. Here, approximation of such damped oscillation in FO systems with the conventional notion of integer order damping and time constant has been carried out using Genetic Algorithm (GA). Next, a multilayer feed-forward Artificial Neural Network (ANN) has been trained using the GA based results to predict the optimal pseudo and meta-damping from knowledge of the maximum order or number of terms in the FO dynamical system.
  • Keywords
    approximation theory; damping; genetic algorithms; multilayer perceptrons; neurocontrollers; nonlinear dynamical systems; approximation; artificial neural network based prediction; damped oscillation; genetic algorithm; integer order damping; metadamping; multilayer feedforward artificial neural network; optimal pseudodamping; oscillatory fractional order dynamical systems; time constant; Computer languages; Damping; Genetic algorithms; Artificial Neural Network (ANN); Genetic Algorithm; fractional order linear systems; meta-damping; pseudo-damping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on
  • Conference_Location
    Nagapattinam, Tamil Nadu
  • Print_ISBN
    978-1-4673-0213-5
  • Type

    conf

  • Filename
    6216029