DocumentCode :
3597478
Title :
A PSO-GD-based hybrid algorithm for general fuzzy measure determination
Author :
Zhao, Huan-yu ; Wang, Xi-Zhao
Author_Institution :
Key Lab. for Machine Learning & Comput. Intell., Hebei Univ., Baoding, China
Volume :
1
fYear :
2009
Firstpage :
553
Lastpage :
556
Abstract :
Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.
Keywords :
fuzzy set theory; gradient methods; particle swarm optimisation; PSO-GD-based hybrid algorithm; general fuzzy measure determination; gradient descent algorithm; particle swarm optimization; Algorithm design and analysis; Computational intelligence; Computer science; Cybernetics; Educational institutions; Machine learning; Machine learning algorithms; Mathematics; Particle measurements; Particle swarm optimization; Fuzzy integral; Fuzzy measure; Gradient descent algorithm; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
Type :
conf
DOI :
10.1109/ICMLC.2009.5212478
Filename :
5212478
Link To Document :
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