TY  - CONF
N1  - cited By 6; Conference of IEEE International Symposium on Robotics and Intelligent Sensors, IRIS 2016 ; Conference Date: 17 December 2016 Through 20 December 2016; Conference Code:134518
N2  - 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.
ID  - scholars7360
TI  - Intelligent Prediction System for Gas Metering System using Particle Swarm Optimization in Training Neural Network
SP  - 165
KW  - Forecasting; Genetic algorithms; Intelligent systems; Neural networks; Particle swarm optimization (PSO)
KW  -  Gas metering system; Intelligent prediction; Metering systems; Neural network prediction model; Neural-networks; Particle swarm; Particle swarm optimization; Prediction modelling; Prediction systems; Swarm optimization
KW  -  Gases
AV  - none
A1  - Rosli, N.S.
A1  - Ibrahim, R.
A1  - Ismail, I.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016089305&doi=10.1016%2fj.procs.2017.01.197&partnerID=40&md5=e3dafb2210ecea949a7fa9ffb65701c1
EP  - 169
VL  - 105
Y1  - 2016///
PB  - Elsevier B.V.
SN  - 18770509
ER  -