eprintid: 8849
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/88/49
datestamp: 2023-11-09 16:20:46
lastmod: 2023-11-09 16:20:46
status_changed: 2023-11-09 16:13:41
type: conference_item
metadata_visibility: show
creators_name: Rosli, N.
creators_name: Ibrahim, R.
creators_name: Ismail, I.
creators_name: Hassan, S.M.
creators_name: Chung, T.D.
title: Neural network architecture selection for efficient prediction model of gas metering system
ispublished: pub
keywords: Computer architecture; Forecasting; Gas meters; Manufacture; Multilayer neural networks; Neural networks; Particle swarm optimization (PSO); Radial basis function networks; Robotics, Efficient predictions; Elman neural network; Generalized Regression Neural Network(GRNN); Metering systems; Multi layer perceptron; Neural networks architecture; Prediction model; Radial Basis Function(RBF), Network architecture
note: cited By 3; Conference of 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016 ; Conference Date: 25 September 2016 Through 27 September 2016; Conference Code:126431
abstract: 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.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922154&doi=10.1109%2fROMA.2016.7847805&partnerID=40&md5=b626301db6310a9f886f2c343f3544b6
id_number: 10.1109/ROMA.2016.7847805
full_text_status: none
publication: 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016
refereed: TRUE
isbn: 9781509009282
citation:   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.