• DocumentCode
    2985965
  • Title

    Research on Nonlinear Predictive Control Rolling Optimization Strategy Based on SAPSO

  • Author

    Bi-ying, Zhou

  • Author_Institution
    Sch. of Math. & Inf. Sci., Weinan Teachers Univ., Weinan, China
  • fYear
    2011
  • fDate
    3-4 Dec. 2011
  • Firstpage
    131
  • Lastpage
    134
  • Abstract
    The problem in neural network model based nonlinear systems predictive control the predictive control law is difficult to get, this paper proposed using simulated annealing particle swarm optimization algorithm (SAPSO) to optimize the solution. Compared particle swarm optimization (PSO) algorithm with SAPSO algorithm in the performance of simulation, using SAPSO algorithm optimized neural network predictive control, simulation results show that the method can effectively reduce the number of iterations and improve convergence accuracy.
  • Keywords
    iterative methods; neurocontrollers; nonlinear control systems; particle swarm optimisation; predictive control; simulated annealing; SAPSO algorithm; iteration method; neural network model; nonlinear predictive control rolling optimization strategy; optimized neural network predictive control; predictive control law; simulated annealing particle swarm optimization algorithm; Algorithm design and analysis; Particle swarm optimization; Prediction algorithms; Predictive control; Predictive models; Simulated annealing; BP neural network; Nonlinear; Optimization; SAPSO;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
  • Conference_Location
    Hainan
  • Print_ISBN
    978-1-4577-2008-6
  • Type

    conf

  • DOI
    10.1109/CIS.2011.37
  • Filename
    6128090