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Functional modelling of large scattered data sets using neural networks

conference contribution
posted on 10.02.2016 by Qinggang Meng, Baihua Li, Nicholas Costen, Horst Holstein
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.

History

School

  • Science

Department

  • Computer Science

Published in

17th International Conference on Artificial Neural Networks (ICANN 2007) Artificial Neural Networks - ICANN 2007, Pt 1, Proceedings

Volume

4668

Pages

441 - 449 (9)

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

Publisher

© Springer-Verlag Berlin Heidelberg

Version

NA (Not Applicable or Unknown)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2007

Notes

This paper is closed access.

ISBN

9783540746898

ISSN

0302-9743

Book series

Theoretical Computer Science and General Issues;4668

Language

en

Location

Oporto, PORTUGAL

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