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
Link To Document :
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