New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series.

Yeh, W-C

New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series.

Yeh, W-C

Permalink

Publication Type:: Journal Article
Citation:: IEEE Trans Neural Netw Learn Syst, 2013, 24 (4), pp. 661 - 665
Issue Date:: 2013-04

Closed Access

	Filename	Description	Size
	2012001174OK.pdf	Published Version	164.42 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yeh, W-C	en_US
dc.date.issued	2013-04	en_US
dc.identifier.citation	IEEE Trans Neural Netw Learn Syst, 2013, 24 (4), pp. 661 - 665	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30714
dc.description.abstract	A new soft computing method called the parameter-free simplified swarm optimization (SSO)-based artificial neural network (ANN), or improved SSO for short, is proposed to adjust the weights in ANNs. The method is a modification of the SSO, and seeks to overcome some of the drawbacks of SSO. In the experiments, the iSSO is compared with five other famous soft computing methods, including the backpropagation algorithm, the genetic algorithm, the particle swarm optimization (PSO) algorithm, cooperative random learning PSO, and the SSO, and its performance is tested on five famous time-series benchmark data to adjust the weights of two ANN models (multilayer perceptron and single multiplicative neuron model). The experimental results demonstrate that iSSO is robust and more efficient than the other five algorithms.	en_US
dc.language	eng	en_US
dc.relation.ispartof	IEEE Trans Neural Netw Learn Syst	en_US
dc.relation.isbasedon	10.1109/TNNLS.2012.2232678	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Time Factors	en_US
dc.subject.mesh	Forecasting	en_US
dc.subject.mesh	Computing Methodologies	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series.	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	24	en_US
utslib.location.activity	United States	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

A new soft computing method called the parameter-free simplified swarm optimization (SSO)-based artificial neural network (ANN), or improved SSO for short, is proposed to adjust the weights in ANNs. The method is a modification of the SSO, and seeks to overcome some of the drawbacks of SSO. In the experiments, the iSSO is compared with five other famous soft computing methods, including the backpropagation algorithm, the genetic algorithm, the particle swarm optimization (PSO) algorithm, cooperative random learning PSO, and the SSO, and its performance is tested on five famous time-series benchmark data to adjust the weights of two ANN models (multilayer perceptron and single multiplicative neuron model). The experimental results demonstrate that iSSO is robust and more efficient than the other five algorithms.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/30714