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