relation: https://khub.utp.edu.my/scholars/7360/
title: Intelligent Prediction System for Gas Metering System using Particle Swarm Optimization in Training Neural Network
creator: Rosli, N.S.
creator: Ibrahim, R.
creator: Ismail, I.
description: In this paper, a study on development of prediction model based on an intelligent systems is discussed for gas metering system in order to validate the instrument reliability. In providing reliable measurement of gas metering system, an accurate prediction model is required for model validation and parameter estimation. The intelligent prediction system has been developed for gas measurement validation. Then the project focused on the application of particle swarm optimization (PSO) and Genetic Algorithm (GA) in training neural network prediction model in enhancing the performance of Intelligent Prediction System (IPS). In this study, the three experiment has been conducted to improve the accuracy of the neural network prediction model. The comparison of the performance of PSONN and GANN with pure ANN is presented in this paper. The results shows that the proposed PSONN model give promising results in the prediction accuracy of gas measurement. © 2017 The Authors.
publisher: Elsevier B.V.
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Rosli, N.S. and Ibrahim, R. and Ismail, I.  (2016) Intelligent Prediction System for Gas Metering System using Particle Swarm Optimization in Training Neural Network.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016089305&doi=10.1016%2fj.procs.2017.01.197&partnerID=40&md5=e3dafb2210ecea949a7fa9ffb65701c1
relation: 10.1016/j.procs.2017.01.197
identifier: 10.1016/j.procs.2017.01.197