Welcome to the InfoSci Platform
Reference Hub4
An Effective Solution to Regression Problem by RBF Neuron Network

An Effective Solution to Regression Problem by RBF Neuron Network

Dang Thi Thu Hien, Hoang Xuan Huan, Le Xuan Minh Hoang
Copyright: © 2015 |Volume: 6 |Issue: 4 |Pages: 18
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781466678026|DOI: 10.4018/IJORIS.2015100104
Cite Article Cite Article

MLA

Hien, Dang Thi Thu, et al. "An Effective Solution to Regression Problem by RBF Neuron Network." IJORIS vol.6, no.4 2015: pp.57-74. http://doi.org/10.4018/IJORIS.2015100104

APA

Hien, D. T., Huan, H. X., & Hoang, L. X. (2015). An Effective Solution to Regression Problem by RBF Neuron Network. International Journal of Operations Research and Information Systems (IJORIS), 6(4), 57-74. http://doi.org/10.4018/IJORIS.2015100104

Chicago

Hien, Dang Thi Thu, Hoang Xuan Huan, and Le Xuan Minh Hoang. "An Effective Solution to Regression Problem by RBF Neuron Network," International Journal of Operations Research and Information Systems (IJORIS) 6, no.4: 57-74. http://doi.org/10.4018/IJORIS.2015100104

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.