A multilayer self-organizing model for convex-hull computation

Author

Pal, Srimanta ; Datta, Amitava ; Pal, Nikhil R.

Author_Institution

Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta, India

Volume

12

Issue

6

fYear

2001

fDate

11/1/2001 12:00:00 AM

Firstpage

1341

Lastpage

1347

Abstract

A self-organizing neural-network model is proposed for computation of the convex-hull of a given set of planar points. The network evolves in such a manner that it adapts itself to the hull-vertices of the convex-hull. The proposed network consists of three layers of processors. The bottom layer computes some angles which are passed to the middle layer. The middle layer is used for computation of the minimum angle (winner selection). These information are passed to the topmost layer as well as fed back to the bottom layer. The network in the topmost layer self-organizes by labeling the hull-processors in an orderly fashion so that the final convex-hull is obtained from the topmost layer. Time complexity of the proposed model is analyzed and is compared with existing models of similar nature

Keywords

computational complexity; convex programming; multilayer perceptrons; self-organising feature maps; convex-hull computation; hull-processors; multilayer self-organizing model; self-organizing neural-network model; time complexity; winner selection; Computational modeling; Computer networks; Image analysis; Labeling; Neural networks; Nonhomogeneous media; Pattern analysis; Rubber; Shape; Two dimensional displays;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.963770

Filename

963770