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Lecture Notes in Computer Science
We propose a self-organising hierarchical Radial Basis Function (RBF) network for functional modelling of large amounts of scattered unstructured point data. The network employs an error-driven active learning algorithm and a multi-layer architecture, allowing progressive bottom-up reinforcement of local features in subdivisions of error clusters. For each RBF subnet, neurons can be inserted, removed or updated iteratively with full dimensionality adapting to the complexity and distribution ofdoi:10.1007/978-3-540-74690-4_45 fatcat:tnmcgrxpnndsdigobqmqjpevuu