eprintid: 1634
rev_number: 6
eprint_status: archive
userid: 11
dir: disk0/00/00/16/34
datestamp: 2016-06-02 03:53:35
lastmod: 2016-06-02 03:53:35
status_changed: 2016-06-02 03:53:35
type: article
metadata_visibility: show
creators_name: Hien, Dang Thi Thu
creators_name: Huan, Hoang Xuan
creators_name: Hoang, Le Xuan Minh
title: An Effective Solution to Regression Problem by RBF Neuron Network
ispublished: pub
subjects: IT
abstract: Radial Basis Function RBF neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, the authors use k-nearest neighbour method to define the value of regression function at the center and an interpolation RBF network training algorithm with equally spaced nodes to train the network. The experiments show the outstanding efficiency of regression function when the training data has Gauss white noise.
date: 2015
date_type: published
official_url: http://dx.doi.org/10.4018/IJORIS.2015100104
id_number: doi:10.4018/IJORIS.2015100104
full_text_status: none
publication: International Journal of Operations Research and Information Systems
volume: 6
number: 4
pagerange: 57-74
refereed: TRUE
issn: 1947-9328
citation:   Hien, Dang Thi Thu and Huan, Hoang Xuan and Hoang, Le Xuan Minh  (2015) An Effective Solution to Regression Problem by RBF Neuron Network.  International Journal of Operations Research and Information Systems, 6  (4).   pp. 57-74.  ISSN 1947-9328