On the regularisation-enhanced training of RBF networks

The radial basis functions (RBF) network is important in neuro-fuzzy systems. In particular, their common universal approximator properties make fuzzy systems as well as neural network systems excellent representations for system modelling. In data-based modelling it is important that the overfitting should be avoided to enhance the generalisation capability of the model since this is an ultimate performance measure for the validity of the model. In this respect, in the majority of the reported researches with RBF networks, the issue of overfitting is omitted and it is attempted to make modelling errors vanish at the price of hidden degradation in generalisation properties of the network. The work addresses this issue in the RBF neural networks for enhanced neuro-fuzzy system modelling