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.

Original languageEnglish
Title of host publicationProceedings of the 7th SEAMS UGM International Conference on Mathematics and Its Applications 2015
Subtitle of host publicationEnhancing the Role of Mathematics in Interdisciplinary Research
EditorsYeni Susanti, Indah Emilia Wijayanti, Fajar Adi Kusumo, Irwan Endrayanto Aluicius
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735413542
DOIs
Publication statusPublished - 11 Feb 2016
Event7th SEAMS UGM International Conference on Mathematics and Its Applications: Enhancing the Role of Mathematics in Interdisciplinary Research - Yogyakarta, Indonesia
Duration: 18 Aug 201521 Aug 2015

Publication series

NameAIP Conference Proceedings
Volume1707
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference7th SEAMS UGM International Conference on Mathematics and Its Applications: Enhancing the Role of Mathematics in Interdisciplinary Research
Country/TerritoryIndonesia
CityYogyakarta
Period18/08/1521/08/15

Keywords

  • BUGS
  • Bayesian
  • MBGRNN
  • MCMC
  • Mixture

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