• DocumentCode
    2764286
  • Title

    Parameters estimation for nonlinear moving average model using Supernova metaheuristic

  • Author

    Delgado, Eddy Mesa ; Cogollo, Myladis R. ; Velásquez, Juan David

  • Author_Institution
    Fac. de Minas, Univ. Nac. de Colombia, Medellín, Colombia
  • fYear
    2012
  • fDate
    1-5 Oct. 2012
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In the practice, to find the parameters for nonlinear Moving average(NLMA) models is difficult because there are not analytical way to solve maximum likelihood function. In this paper, we compare the parameters estimated for NLMA model with Supernova and PSO-DE algorithm. We found that Supernova estimation is better in terms of a reduction of error and nearest solutions to optimal point.
  • Keywords
    evolutionary computation; maximum likelihood estimation; moving average processes; particle swarm optimisation; NLMA models; PSO-DE algorithm; maximum likelihood function; nonlinear moving average model; parameters estimation; supernova metaheuristic; Computational modeling; Educational institutions; Electric shock; Maximum likelihood estimation; Silicon compounds; Time series analysis; Vectors; Metaheuristics; Supernova; intelligent systems; moving average; optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing Congress (CCC), 2012 7th Colombian
  • Conference_Location
    Medellin
  • Print_ISBN
    978-1-4673-1475-6
  • Type

    conf

  • DOI
    10.1109/ColombianCC.2012.6398019
  • Filename
    6398019
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2764286