TY  - JOUR
ID  - SisLab1634
UR  - http://dx.doi.org/10.4018/IJORIS.2015100104
IS  - 4
A1  - Hien, Dang Thi Thu
A1  - Huan, Hoang Xuan
A1  - Hoang, Le Xuan Minh
Y1  - 2015///
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.
JF  - International Journal of Operations Research and Information Systems
VL  - 6
SN  - 1947-9328
TI  - An Effective Solution to Regression Problem by RBF Neuron Network
SP  - 57
AV  - none
EP  - 74
ER  -