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Reseach Article

A NonñLinear Feedback Neural Network for Graph Coloring

by Mohd. Samar Ansari, Syed Atiqur Rahman, Syed Javed Arif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 16
Year of Publication: 2012
Authors: Mohd. Samar Ansari, Syed Atiqur Rahman, Syed Javed Arif
10.5120/4906-7417

Mohd. Samar Ansari, Syed Atiqur Rahman, Syed Javed Arif . A NonñLinear Feedback Neural Network for Graph Coloring. International Journal of Computer Applications. 39, 16 ( February 2012), 31-33. DOI=10.5120/4906-7417

@article{ 10.5120/4906-7417,
author = { Mohd. Samar Ansari, Syed Atiqur Rahman, Syed Javed Arif },
title = { A NonñLinear Feedback Neural Network for Graph Coloring },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 16 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number16/4906-7417/ },
doi = { 10.5120/4906-7417 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:37.446755+05:30
%A Mohd. Samar Ansari
%A Syed Atiqur Rahman
%A Syed Javed Arif
%T A NonñLinear Feedback Neural Network for Graph Coloring
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 16
%P 31-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A feedback neural network for solving graph coloring problem is presented. The circuit has an associated transcendental energy function that ensures fast convergence to the exact solution. Hardware and PSPICE simulation results on random and benchmark problems have been presented. Test results are compared with existing techniques for graph coloring to show that the proposed neural network model provides a significant reduction in the number of colors while enjoying a simple and efficient circuit implementation.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Neural network applications Neural network hardware Non-linear circuits Graph theory Dynamical Systems Non-Linear Synapse Feedback Networks