Abstract
A multi-layer self-organizing neural network model has been proposed for computation of the convex-layers of a given set of planar points. Computation of convex-layers has been found to be useful in pattern recognition and in statistics. The proposed network architecture evolves in such a manner that it adapts itself to the hull-vertices of the convex-layers in the required order. Time complexity of the proposed model is also discussed.
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References
Akl, S.G., Lyons, K.A.: Parallel Computational Geometry. Prentice-Hall, Englewood Cliffs (1993)
Chazelle, B.: On the convex layers of a Planar set. IEEE Trans. on Info. Theory IT-31(4) (1985)
Chazelle, B., Guibas, L., Lee, D.T.: The power of geometric duality. In: Proc. 24th IEEE Ann. Symp. Foundations of Computer Science, pp. 217–225 (1983)
Green, P.J., Silverman, B.W.: Constructing the convex hull of a set of points in the plane. Comput. J. 22, 262–266 (1979)
Hadley, G.: Linear Programming. Addison-Wesley Publishing Company, Reading (1962)
Simon, H.: Neural networks. Prentice Hall, New Jersey (1994)
Huber, P.J.: Robust statistics: a review. Ann. Math. Statist. 43(3), 1041–1067 (1972)
Overmars, M.H., van Leeuwen, J.: Maintenance of configurations in the plane. J. Comput. Syst. Sci. 23, 166–204 (1981)
Preparata, F.P., Shamos, M.I.: Computational Geometry: An Introduction. Springer, New York (1985)
Pal, S., Datta, A., Pal, N.R.: A multi-layer self-organizing model for convexhull computation. IEEE Trans. Neural Network 12, 1341–1347 (2001)
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Datta, A., Pal, S. (2004). Computing Convex-Layers by a Multi-layer Self-organizing Neural Network. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_99
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DOI: https://doi.org/10.1007/978-3-540-30499-9_99
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