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Koutroumbas K.,Institute for Space Applications and Remote Sensing | Bakopoulos Y.,Applied Computational Technologies
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

In this paper the problem of the approximation of decision regions bordered by (a) closed and/or (b) open and unbounded convex hypersurfaces using feedforward neural networks (FNNs) with hard limiter nodes is considered. Specifically, a constructive proof is given for the fact that a two or a three layer FNN with hard limiter nodes can approximate with arbitrary precision a given decision region of the above kind. This is carried out in three steps. First, each hypersurface is approximated by hyperplanes. Then each one of the regions formed by the hypersurfaces is appropriately approximated by regions defined via the previous hyperplanes. Finally, a feedforward neural network with hard limiter nodes is constructed, based on the previous hyperplanes and the regions defined by them. © Springer-Verlag Berlin Heidelberg 2010.


Applied Computational Technologies | Entity website


Applied Computational Technologies | Entity website

http://www.appcomptech ...


Applied Computational Technologies | Entity website


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