An Accurate PSO-GA Based Neural Network to Model Growth of Carbon Nanotubes

Asadnia, M; Khorasani, AM; Warkiani, ME

An Accurate PSO-GA Based Neural Network to Model Growth of Carbon Nanotubes

Asadnia, M Khorasani, AM Warkiani, ME

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Publication Type:: Journal Article
Citation:: Journal of Nanomaterials, 2017, 2017
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Asadnia, M	en_US
dc.contributor.author	Khorasani, AM	en_US
dc.contributor.author	Warkiani, ME https://orcid.org/0000-0002-4184-1944	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Journal of Nanomaterials, 2017, 2017	en_US
dc.identifier.issn	1687-4110	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125610
dc.description.abstract	© 2017 Mohsen Asadnia et al. By combining particle swarm optimization (PSO) and genetic algorithms (GA) this paper offers an innovative algorithm to train artificial neural networks (ANNs) for the purpose of calculating the experimental growth parameters of CNTs. The paper explores experimentally obtaining data to train ANNs, as a method to reduce simulation time while ensuring the precision of formal physics models. The results are compared with conventional particle swarm optimization based neural network (CPSONN) and Levenberg-Marquardt (LM) techniques. The results show that PSOGANN can be successfully utilized for modeling the experimental parameters that are critical for the growth of CNTs.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP170103704
dc.relation.ispartof	Journal of Nanomaterials	en_US
dc.relation.isbasedon	10.1155/2017/9702384	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Nanoscience & Nanotechnology	en_US
dc.title	An Accurate PSO-GA Based Neural Network to Model Growth of Carbon Nanotubes	en_US
dc.type	Journal Article
utslib.citation.volume	2017	en_US
utslib.for	1007 Nanotechnology	en_US
utslib.for	0912 Materials Engineering	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2017	en_US

Abstract:

© 2017 Mohsen Asadnia et al. By combining particle swarm optimization (PSO) and genetic algorithms (GA) this paper offers an innovative algorithm to train artificial neural networks (ANNs) for the purpose of calculating the experimental growth parameters of CNTs. The paper explores experimentally obtaining data to train ANNs, as a method to reduce simulation time while ensuring the precision of formal physics models. The results are compared with conventional particle swarm optimization based neural network (CPSONN) and Levenberg-Marquardt (LM) techniques. The results show that PSOGANN can be successfully utilized for modeling the experimental parameters that are critical for the growth of CNTs.

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http://hdl.handle.net/10453/125610