Artificial neural networks parameters optimization design of experiments: An application in materials modeling

This paper focused on the application of design of experiments to determine optimize parameters for multilayer-perceptron artificial neural network trained with back-propagation for modeling purpose. Artificial neural networks (ANNs) for modeling have been widely used in various fields because of it...

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Main Authors: Laosiritaworn W., Chotchaithanakorn N.
Format: Article
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-67650323958&partnerID=40&md5=b311ad2afb5d9a15fcb1d30c40626b8f
http://cmuir.cmu.ac.th/handle/6653943832/1453
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-14532014-08-29T09:29:19Z Artificial neural networks parameters optimization design of experiments: An application in materials modeling Laosiritaworn W. Chotchaithanakorn N. This paper focused on the application of design of experiments to determine optimize parameters for multilayer-perceptron artificial neural network trained with back-propagation for modeling purpose. Artificial neural networks (ANNs) for modeling have been widely used in various fields because of its ability to 'learn' from examples. The accuracy of ANN model depends very much on the setting of network parameters, such as number of neurons, number of hidden layers and learning rate. Most literatures in this area suggested trial-anderror method for parameters setting which are time consuming and non economical, whereas the optimal setting cannot be guaranteed. Consequently, design of experiment techniques is generally required to optimize various processes. In this paper, as a case study, it was used to find optimum setting of ANN trained to model ferromagnetic material data. Interested characteristic was finite-sized ferromagnetic Curie temperature obtained from Monte Carlo simulation on two dimensional Ising spins. The results indicated that design of experiments is a promising solution to the mentioned problem. The issues arising from this case were also discussed. 2014-08-29T09:29:19Z 2014-08-29T09:29:19Z 2009 Article 01252526 http://www.scopus.com/inward/record.url?eid=2-s2.0-67650323958&partnerID=40&md5=b311ad2afb5d9a15fcb1d30c40626b8f http://cmuir.cmu.ac.th/handle/6653943832/1453 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description This paper focused on the application of design of experiments to determine optimize parameters for multilayer-perceptron artificial neural network trained with back-propagation for modeling purpose. Artificial neural networks (ANNs) for modeling have been widely used in various fields because of its ability to 'learn' from examples. The accuracy of ANN model depends very much on the setting of network parameters, such as number of neurons, number of hidden layers and learning rate. Most literatures in this area suggested trial-anderror method for parameters setting which are time consuming and non economical, whereas the optimal setting cannot be guaranteed. Consequently, design of experiment techniques is generally required to optimize various processes. In this paper, as a case study, it was used to find optimum setting of ANN trained to model ferromagnetic material data. Interested characteristic was finite-sized ferromagnetic Curie temperature obtained from Monte Carlo simulation on two dimensional Ising spins. The results indicated that design of experiments is a promising solution to the mentioned problem. The issues arising from this case were also discussed.
format Article
author Laosiritaworn W.
Chotchaithanakorn N.
spellingShingle Laosiritaworn W.
Chotchaithanakorn N.
Artificial neural networks parameters optimization design of experiments: An application in materials modeling
author_facet Laosiritaworn W.
Chotchaithanakorn N.
author_sort Laosiritaworn W.
title Artificial neural networks parameters optimization design of experiments: An application in materials modeling
title_short Artificial neural networks parameters optimization design of experiments: An application in materials modeling
title_full Artificial neural networks parameters optimization design of experiments: An application in materials modeling
title_fullStr Artificial neural networks parameters optimization design of experiments: An application in materials modeling
title_full_unstemmed Artificial neural networks parameters optimization design of experiments: An application in materials modeling
title_sort artificial neural networks parameters optimization design of experiments: an application in materials modeling
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-67650323958&partnerID=40&md5=b311ad2afb5d9a15fcb1d30c40626b8f
http://cmuir.cmu.ac.th/handle/6653943832/1453
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