@inproceedings{6f80335a834648448318b4a789462696,
title = "Parameter estimation of general regression neural network using Bayesian approach",
abstract = "General Regression Neural Network (GRNN) has been applied in a large number of forecasting/prediction problem. Generally, there are two types of GRNN: GRNN which is based on kernel density; and Mixture Based GRNN (MBGRNN) which is based on adaptive mixture model. The main problem on GRNN modeling lays on how its parameters were estimated. In this paper, we propose Bayesian approach and its computation using Markov Chain Monte Carlo (MCMC) algorithms for estimating the MBGRNN parameters. This method is applied in simulation study. In this study, its performances are measured by using MAPE, MAE and RMSE. The application of Bayesian method to estimate MBGRNN parameters using MCMC is straightforward but it needs much iteration to achieve convergence.",
keywords = "BUGS, Bayesian, MBGRNN, MCMC, Mixture",
author = "Choir, {Achmad Syahrul} and Prasetyo, {Rindang Bangun} and Ulama, {Brodjol Sutijo Suprih} and Nur Iriawan and Kartika Fitriasari and Mohammad Dokhi",
note = "Publisher Copyright: {\textcopyright} 2016 AIP Publishing LLC.; 7th SEAMS UGM International Conference on Mathematics and Its Applications: Enhancing the Role of Mathematics in Interdisciplinary Research ; Conference date: 18-08-2015 Through 21-08-2015",
year = "2016",
month = feb,
day = "11",
doi = "10.1063/1.4940858",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Yeni Susanti and Wijayanti, {Indah Emilia} and Kusumo, {Fajar Adi} and Aluicius, {Irwan Endrayanto}",
booktitle = "Proceedings of the 7th SEAMS UGM International Conference on Mathematics and Its Applications 2015",
}