Abstract
In this paper a new connectionist model is proposed. The proposed architecture is trained by a scheme based on partition of the function domain, approximating the generator function by a set of very simple supporting functions. This method has an interesting ability concerning interpolation. A synthetic experiment and areal data missing data application are presented.
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References
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© 2001 Springer-Verlag Wien
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Pedreira, C.E., Pedroza, L.C., Fariñas, M. (2001). Local-Global Neural Networks for Interpolation. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_12
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_12
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
eBook Packages: Springer Book Archive