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

Mohsen Asadnia; Amir Mahyar Khorasani; Majid Ebrahimi Warkiani

doi:10.1155/2017/9702384

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

Mohsen Asadnia^*, Amir Mahyar Khorasani, Majid Ebrahimi Warkiani

^*Corresponding author for this work

School of Engineering

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

9 Downloads (Pure)

Abstract

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.

Original language	English
Article number	9702384
Pages (from-to)	1-6
Number of pages	6
Journal	Journal of Nanomaterials
Volume	2017
DOIs	https://doi.org/10.1155/2017/9702384
Publication status	Published - 2017

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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10.1155/2017/9702384Licence: CC BY

Publisher version (open access)Final published version, 654 KBLicence: CC BY

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Cite this

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