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Functional modelling of large scattered data sets using neural networks
conference contribution
posted on 2016-02-10, 12:09 authored by Qinggang MengQinggang Meng, Baihua LiBaihua Li, Nicholas Costen, Horst HolsteinWe 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, ProceedingsVolume
4668Pages
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.441-449Publisher
© Springer-Verlag Berlin HeidelbergVersion
- 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
2007Notes
This paper is closed access.ISBN
9783540746898ISSN
0302-9743Publisher version
Book series
Theoretical Computer Science and General Issues;4668Language
- en