• DocumentCode
    1713067
  • Title

    Neurofuzzy inference systems based on tribal particle swarm optimization for forecasting sunspot numbers

  • Author

    Cheng-hung Chen ; Yen-Yun Liao ; Shu-Wei Liu

  • Author_Institution
    Dept. of Electr. Eng., Nat. Formosa Univ., Yunlin, Taiwan
  • fYear
    2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the specific neurofuzzy inference system (NIS) for forecasting sunspot numbers. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.
  • Keywords
    astronomy computing; fuzzy neural nets; fuzzy reasoning; least mean squares methods; particle swarm optimisation; sunspots; NIS; RMS error; TOA; TPSO; evolution strategies; neurofuzzy inference system; search space global exploration; search space local exploration; sunspot number forecasting; tribal particle swarm optimization; tribes optimization algorithm; Forecasting; Fuzzy systems; Genetic algorithms; Input variables; Optimization; Particle swarm optimization; neurofuzzy inference systems; particle swarm optimization; prediction; tribes optimization algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on
  • Conference_Location
    Tainan
  • Print_ISBN
    978-1-4799-0433-4
  • Type

    conf

  • DOI
    10.1109/ICICS.2013.6782878
  • Filename
    6782878