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Neural network architecture selection for efficient prediction model of gas metering system

This paper presents a comparative study and analysis of different neural network architectures of which one will be recommended towards adoption for developing a prediction model for gas metering system. Thus, the focus of this paper is to select the most suitable neural network architecture for gas...

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Main Authors: Rosli, N., Ibrahim, R., Ismail, I., Hassan, S.M., Chung, T.D.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922154&doi=10.1109%2fROMA.2016.7847805&partnerID=40&md5=b626301db6310a9f886f2c343f3544b6
http://eprints.utp.edu.my/20146/
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spelling my.utp.eprints.201462018-04-22T14:43:19Z Neural network architecture selection for efficient prediction model of gas metering system Rosli, N. Ibrahim, R. Ismail, I. Hassan, S.M. Chung, T.D. This paper presents a comparative study and analysis of different neural network architectures of which one will be recommended towards adoption for developing a prediction model for gas metering system. Thus, the focus of this paper is to select the most suitable neural network architecture for gas metering system prediction model. A few neural networks architecture are modeled and simulated; Radial basis Function (RBF), Multilayer Perceptron (MLP), Elman Network, Generalized Regression Neural Networks (GRNN) and Elman Neural Network. In order to select the best architecture, the performance of the various networks considered are compared. From the results obtained, the network architecture that results in the best performance is the RBF network structure. Hence recommended for adoption for the design. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922154&doi=10.1109%2fROMA.2016.7847805&partnerID=40&md5=b626301db6310a9f886f2c343f3544b6 Rosli, N. and Ibrahim, R. and Ismail, I. and Hassan, S.M. and Chung, T.D. (2017) Neural network architecture selection for efficient prediction model of gas metering system. 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016 . http://eprints.utp.edu.my/20146/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper presents a comparative study and analysis of different neural network architectures of which one will be recommended towards adoption for developing a prediction model for gas metering system. Thus, the focus of this paper is to select the most suitable neural network architecture for gas metering system prediction model. A few neural networks architecture are modeled and simulated; Radial basis Function (RBF), Multilayer Perceptron (MLP), Elman Network, Generalized Regression Neural Networks (GRNN) and Elman Neural Network. In order to select the best architecture, the performance of the various networks considered are compared. From the results obtained, the network architecture that results in the best performance is the RBF network structure. Hence recommended for adoption for the design. © 2016 IEEE.
format Article
author Rosli, N.
Ibrahim, R.
Ismail, I.
Hassan, S.M.
Chung, T.D.
spellingShingle Rosli, N.
Ibrahim, R.
Ismail, I.
Hassan, S.M.
Chung, T.D.
Neural network architecture selection for efficient prediction model of gas metering system
author_facet Rosli, N.
Ibrahim, R.
Ismail, I.
Hassan, S.M.
Chung, T.D.
author_sort Rosli, N.
title Neural network architecture selection for efficient prediction model of gas metering system
title_short Neural network architecture selection for efficient prediction model of gas metering system
title_full Neural network architecture selection for efficient prediction model of gas metering system
title_fullStr Neural network architecture selection for efficient prediction model of gas metering system
title_full_unstemmed Neural network architecture selection for efficient prediction model of gas metering system
title_sort neural network architecture selection for efficient prediction model of gas metering system
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922154&doi=10.1109%2fROMA.2016.7847805&partnerID=40&md5=b626301db6310a9f886f2c343f3544b6
http://eprints.utp.edu.my/20146/
_version_ 1738656170206298112
score 13.211869

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