Growing radial basis neural networks

This paper proposes a framework for constructing and training growing radial basis function (GRBF) neural networks. The GRBF network grows in the process of training by splitting one of the prototypes that determine the locations of the radial basis functions. Two splitting criteria are proposed to determine which prototype to split at each growing cycle. The proposed hybrid learning scheme provides the framework for incorporating existing algorithms in the training of GRBF networks. A supervised learning scheme based on the minimization of the localized class-conditional variance is also proposed. GRBF neural networks are evaluated and tested on pattern classification applications with very satisfactory results.