Neurocomputing model for computation of an approximate convex hull of a set of points and spheres

被引：3
|
作者：
Pal, Srimanta [1 ]
Bhattacharya, Sabyasachi
机构：
[1] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, W Bengal, India
[2] TATA Consultancy Serv Ltd, Comp Consultant Dept, Kolkata 700091, W Bengal, India
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2007年 / 18卷 / 02期
关键词：
convex hull; energy function; neural networks;
D O I：
10.1109/TNN.2007.891201
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this letter, a two-layer neural network is proposed for computation of an approximate convex hull of a set of given points in 3-D or a set of spheres of different sizes. The algorithm is designed based on an elegant concept-shrinking of a spherical rubber balloon surrounding the set of objects in 3-D. Logically, a set of neurons is orderly placed on a spherical mesh i.e., on a rubber balloon surrounding the objects. Each neuron has a parameter vector associated with its current position. The resultant force of attraction, between a neuron and each of the given points/objects, determines the direction of a movement of the neuron lying on the rubber balloon. As the network evolves, the neurons (parameter vectors) approximate the convex hull more and more accurately.
引用
收藏
页码：600 / 605
页数：6
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