Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques

This paper proposes a framework for constructing and training radial basis function (RBF) neural networks. proposed growing radial basis function (GRBF) network begins with a small number of prototypes which determine the locations of radial basis functions, In the process of training, the GRBF network grows by splitting one of the prototypes at each growing cycle, Two splitting criteria are proposed to determine which prototype to split in each growing cycle, The proposed hybrid learning scheme provides a framework for incorporating existing algorithms in the training of GRBF networks, These include unsupervised algorithms for clustering and learning vector quantization, as well as learning algorithms for training single-layer Linear neural networks, A supervised learning scheme based the minimization of the localized class-conditional variance also proposed and tested, GRBF neural networks are evaluated and tested an a variety of data sets,vith very satisfactory results.