An Effective Solution to Regression Problem by RBF Neuron Network

Dang Thi Thu Hien, Hoang Xuan Huan and Le Xuan Minh Hoang
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Dang Thi Thu Hien: Faculty of Information Technology, University of Transport and Communications, Hanoi, Vietnam
Hoang Xuan Huan: Faculty of Information Technology, Vietnam National University, Hanoi, Vietnam
Le Xuan Minh Hoang: Faculty of Information Technology, Vietnam National University, Hanoi, Vietnam

International Journal of Operations Research and Information Systems (IJORIS), 2015, vol. 6, issue 4, 57-74

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
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Persistent link: https://EconPapers.repec.org/RePEc:igg:joris0:v:6:y:2015:i:4:p:57-74

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