Global and Local Modelling in Radial Basis Functions Networks

In the problem of modelling Input/Output data using neuro-fuzzy systems, the performance of the global model is normally the only objective optimized, and this might cause a misleading performance of the local models. This work presents a modified radial basis function network that maintains the optimization properties of the local sub-models whereas the model is globally optimized, thanks to a special partitioning of the input space in the hidden layer performed to carry out those objectives. The advantage of the methodology proposed is that due to those properties, the global and the local models are both directly optimized. A learning methodology adapted to the proposed model is used in the simulations, consisting of a clustering algorithm for the initialization of the centers and a local search technique.

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