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

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
10 Downloads (Pure)


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 languageEnglish
Article number9702384
Pages (from-to)1-6
Number of pages6
JournalJournal of Nanomaterials
Publication statusPublished - 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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