An effective solution to regression problem by RBF neuron network

Helen Dang; Xuan Huan Hoang

doi:10.4018/IJORIS.2015100104

An effective solution to regression problem by RBF neuron network

Helen Dang
, Xuan Huan Hoang

Mathematics and Computer Studies

Research & Scholarship: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	International Journal of Operations Research and Information Systems (IJORIS)
Volume	6
DOIs	https://doi.org/10.4018/IJORIS.2015100104
State	Published - Oct 1 2015

Keywords

Artificial Neural Network
Multivariate Interpolation
Approximation Function
Basis Functions
Regression k-Nearest Neighbors

Disciplines

Computer Sciences

Access to Document

10.4018/IJORIS.2015100104

Cite this

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title = "An effective solution to regression problem by RBF neuron network",

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. ",

keywords = "Artificial Neural Network, Multivariate Interpolation, Approximation Function, Basis Functions, Regression k-Nearest Neighbors",

author = "Helen Dang and Hoang, \{Xuan Huan\}",

note = "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 peop... H. T. T., Dang, H.X., Hoang and H. M. X., Le (2015), “An effective solution to regression problem by RBF neuron network”, Int. J. of Operation Research and Information System – IJORIS, Vol. 6, Issue 4, p.57-74",

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language = "American English",

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AU - Dang, Helen

AU - Hoang, Xuan Huan

N1 - 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 peop... H. T. T., Dang, H.X., Hoang and H. M. X., Le (2015), “An effective solution to regression problem by RBF neuron network”, Int. J. of Operation Research and Information System – IJORIS, Vol. 6, Issue 4, p.57-74

PY - 2015/10/1

Y1 - 2015/10/1

N2 - 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.

AB - 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.

KW - Artificial Neural Network

KW - Multivariate Interpolation

KW - Approximation Function

KW - Basis Functions

KW - Regression k-Nearest Neighbors

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