A neural network approach to the construction of Delaunay tessellation of points in R/sup d

Since a neural network may be designed directly from either the Delaunay tessellation (DT) or its abstract dual, the Voronoi diagram, the procedure advanced here for training a dynamic feedforward neural network to generate the DT of specified points representing exemplars in multidimensional featur...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. 1, Fundamental theory and applications Fundamental theory and applications, 1994-09, Vol.41 (9), p.611-613
Main Authors: Garga, A.K., Bose, N.K.
Format: Article
Language:English
Subjects:
Design methodology
Feedforward neural networks
Intelligent networks
Multi-layer neural network
Multidimensional systems
Network synthesis
Neural networks
Neurons
Pattern classification
Signal processing algorithms
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Summary:Since a neural network may be designed directly from either the Delaunay tessellation (DT) or its abstract dual, the Voronoi diagram, the procedure advanced here for training a dynamic feedforward neural network to generate the DT of specified points representing exemplars in multidimensional feature space, contributes toward the goal of an all-neural approach to the synthesis of neural networks. As the expected number of simplexes in the DT over n points is linear in n, the procedure is convenient for real-time implementation of pattern classifiers.< >
ISSN:1057-7122
1558-1268
DOI:10.1109/81.317962