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

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 .

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Electrical & Electronic Engineering
ID Code:20146
Deposited By: Ahmad Suhairi
Deposited On:22 Apr 2018 14:43
Last Modified:22 Apr 2018 14:43

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