An improved learning algorithm for compact RBF networks

An improved learning algorithm for RBF networks is presented in this paper. It allocates new neurons by a four-part novelty criterion, removes redundant neurons according to their error reduction rates, and updates output-layer weights by a recursive least-squares algorithm with Givens QR decomposition. Simulations on two benchmark problems demonstrate that the algorithm produces more compact networks than existing algorithms