Improved radial basis function network for the heteroscedasticity noises

The paper presents an improved radial basis function network to degrade the influence of the heteroscedasticity noises in the training data. A general purpose learning algorithm is regarded as the statistical nonlinear regression model which is assumed the constant noise level. However, the heteroscedasticity noises always exist in the real data. The transformation based least trimmed squares-support vector regression radial basis function network (LTS-SVR RBFN) employs the Box-Cox transformation and the LTS-SVR to address this problem. From the experiment results, it is evident that our proposed method can be used in a more realistic data.