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An accurate PSO-GA based neural network to model growth of carbon nanotubes

Asadnia, Mohsen, Khorasani, Amir Mahyar and Warkiani, Majid Ebrahimi 2017, An accurate PSO-GA based neural network to model growth of carbon nanotubes, Journal of Nanomaterials, vol. 2017, doi: 10.1155/2017/9702384.

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Title An accurate PSO-GA based neural network to model growth of carbon nanotubes
Author(s) Asadnia, Mohsen
Khorasani, Amir Mahyar
Warkiani, Majid Ebrahimi
Journal name Journal of Nanomaterials
Volume number 2017
Article ID 9702384
Total pages 7
Publisher Hindawi Publishing
Place of publication Cairo, Egypt
Publication date 2017-01-01
ISSN 1687-4110
1687-4129
Summary 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.
Language eng
DOI 10.1155/2017/9702384
Field of Research 0910 Manufacturing Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30114342

Document type: Journal Article
Collections: School of Engineering
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.