title: Convergent decomposition techniques for training RBF neural networks.
abstract: In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers. Then we define a decomposition algorithm in which the selection of the center locations is split into sequential minimizations with respect to each center, and we give a suitable criterion for choosing the centers that must be updated at each step. We prove the global convergence of the proposed algorithms and report the computational results obtained for a set of test problems.
authors.affiliation: Dipartimento di Informatica e Sistemistica, Università di Roma "La Sapienza," Via Buonarroti 12 00185, Roma, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Buzzi
authors.forename: L
authors.initials: L
authors.lastname: Grippo
authors.forename: M
authors.initials: M
authors.lastname: Sciandrone
mesh_terms: D000465:Algorithms; D007858:Learning; D008433:Mathematics; D008959:Models, Neurological; D016571:Neural Networks, Computer