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Title: Functional modelling of large scattered data sets using neural networks
Authors: Meng, Qinggang
Li, Baihua
Costen, Nicholas
Holstein, Horst
Issue Date: 2007
Publisher: © Springer-Verlag Berlin Heidelberg
Citation: MENG, Q. ... et al, 2007. Functional modelling of large scattered data sets using neural networks. IN: Marques de Sá, J. ... et al (eds). Artificial Neural Networks - ICANN 2007: 17th International Conference on Artificial Neural Networks17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. Theoretical Computer Science and General Issues; 4668. Berlin; Heidelberg: Springer-Verlag, pp.441-449
Series/Report no.: Theoretical Computer Science and General Issues;4668
Abstract: 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 of the underlying data. This flexibility is particularly desirable for highly variable spatial frequencies. Experimental results demonstrate that the network representation is conducive to geometric data formulation and simplification, and therefore to manageable computation and compact storage.
Description: This paper is closed access.
Version: Closed access
DOI: 10.1007/978-3-540-74690-4
URI: https://dspace.lboro.ac.uk/2134/20289
Publisher Link: http://dx.doi.org/10.1007/978-3-540-74690-4
ISBN: 9783540746898
ISSN: 0302-9743
Appears in Collections:Closed Access (Computer Science)

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