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

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, a...

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Bibliographic Details
Published in:Journal of nanomaterials 2017-01, Vol.2017 (2017), p.1-6
Main Authors: Asadnia, Mohsen, Warkiani, Majid Ebrahimi, Khorasani, Amir Mahyar
Format: Article
Language:English
Subjects:
Architectural engineering
Artificial neural networks
Back propagation
Carbon
Carbon nanotubes
Chromosomes
Computer simulation
Engineering
Genetic algorithms
Mathematical models
Microelectromechanical systems
Mutation
Nanocomposites
Nanomaterials
Nanotechnology
Nanotubes
Neural networks
Optimization algorithms
Particle swarm optimization
Performance evaluation
Thin film coatings
Transistors
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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.
ISSN:1687-4110
1687-4129
DOI:10.1155/2017/9702384