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An Effective Solution to Regression Problem by RBF Neuron Network

Author

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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)

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.

Suggested Citation

  • Dang Thi Thu Hien & Hoang Xuan Huan & Le Xuan Minh Hoang, 2015. "An Effective Solution to Regression Problem by RBF Neuron Network," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 6(4), pages 57-74, October.
    • Handle: RePEc:igg:joris0:v:6:y:2015:i:4:p:57-74
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