TY - JOUR
T1 - An accurate PSO-GA based neural network to model growth of carbon nanotubes
AU - Asadnia, Mohsen
AU - Khorasani, Amir Mahyar
AU - Warkiani, Majid Ebrahimi
N1 - 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.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029725167&partnerID=8YFLogxK
U2 - 10.1155/2017/9702384
DO - 10.1155/2017/9702384
M3 - Article
AN - SCOPUS:85029725167
VL - 2017
SP - 1
EP - 6
JO - Journal of Nanomaterials
JF - Journal of Nanomaterials
SN - 1687-4110
M1 - 9702384
ER -