relation: https://khub.utp.edu.my/scholars/8849/
title: Neural network architecture selection for efficient prediction model of gas metering system
creator: Rosli, N.
creator: Ibrahim, R.
creator: Ismail, I.
creator: Hassan, S.M.
creator: Chung, T.D.
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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922154&doi=10.1109%2fROMA.2016.7847805&partnerID=40&md5=b626301db6310a9f886f2c343f3544b6
relation: 10.1109/ROMA.2016.7847805
identifier: 10.1109/ROMA.2016.7847805