Exploiting the separability of linear and nonlinear parameters in radial basis function networks

In intelligent control applications, neural models and controllers are usually designed by performing an off-line training, and then adapting it on-line when placed in the operating environment. Ir is therefore of crucial importance to obtain a good off-line model by means of a good off-line training algorithm. In this paper a method is presented that fully exploits the linear-nonlinear structure found in Radial Basis Function networks, being additionally applicable to other feed-forward supervised neural networks. The new algorithm is compared with two known hybrid methods.