A new learning algorithm for RBF neural networks with applications to nonlinear system identification

We have presented an identification technique for nonlinear discrete-time multivariable dynamical systems based on RBF (Radial Basis Function) neural nets. The ways to fix the neural net structure and the weights are addressed as two different problems with separately developed online algorithms for their determination. At the present stage, the determination of the RBF net structure is still heuristics-based and this may lead to modeling error, and possible breakdown of the weight updating algorithm. There is thus a real need to develop theory that can help to aid the generation of RBF neural net structures