Investigating the use of alternative topologies on performance of the PSO-ELM

In recent years, the Extreme Learning Machine (ELM) has been hybridized with the Particle Swarm Optimization (PSO) and such hybridization is called PSO-ELM. In most of these hybridizations, the PSO uses the Global topology. However, other topologies were designed to improve the performance of the PS...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2014-03, Vol.127, p.4-12
Main Authors: Figueiredo, Elliackin M.N., Ludermir, Teresa B.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Correlation
ELM-PSO
Empirical analysis
Errors
Exact sciences and technology
Extreme learning machine
Neural networks
Optimization
Particle swarm optimization
PSO topology
Roots
Swarm intelligence
Topology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, the Extreme Learning Machine (ELM) has been hybridized with the Particle Swarm Optimization (PSO) and such hybridization is called PSO-ELM. In most of these hybridizations, the PSO uses the Global topology. However, other topologies were designed to improve the performance of the PSO. In the literature, it is well known that the performance of the PSO depends on its topology, and there is not a best topology for all problems. Thus, in this paper, we investigate the effect of eight PSO topologies on performance of the PSO-ELM. The results showed empirically that the Global topology was more promising than all other topologies in optimizing the PSO-ELM according to the root mean squared error (RMSE) on the validation set in most of the evaluated datasets. However, no correlation was detected between this good performance on the RMSE and the testing accuracy.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.05.047