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Convergent Decomposition Techniques for Training RBF Neural Networks

Dipartimento di Informatica e Sistemistica, Università di Roma "La Sapienza," Via Buonarroti 12 00185, Roma, Italy

L. Grippo

Dipartimento di Informatica e Sistemistica, Università di Roma "La Sapienza," Via Buonarroti 12 00185, Roma, Italy

M. Sciandrone

Istituto di Analisi dei Sistemi ed Informatica del CNR, Viale Manzoni 30 - 00185 Roma, Italy

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.