@inproceedings{scholars8849,
            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},
            year = {2017},
             doi = {10.1109/ROMA.2016.7847805},
         journal = {2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Neural network architecture selection for efficient prediction model of gas metering system},
          author = {Rosli, N. and Ibrahim, R. and Ismail, I. and Hassan, S. M. and Chung, T. D.},
            isbn = {9781509009282},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922154&doi=10.1109\%2fROMA.2016.7847805&partnerID=40&md5=b626301db6310a9f886f2c343f3544b6},
        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},
        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. {\^A}{\copyright} 2016 IEEE.}
}