Maximum neural network with nonlinear self-feedback for maximum clique problem

Jiahai Wang; Zheng Tang; Ronglong Wang

doi:10.1016/j.neucom.2004.01.001

Maximum neural network with nonlinear self-feedback for maximum clique problem

Jiahai Wang^*, Zheng Tang, Ronglong Wang

^*Corresponding author for this work

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9 Scopus citations

Abstract

In this paper, by adding a nonlinear self-feedback to the maximum neural network, we propose a parallel algorithm for the maximum clique problem that introduces richer and more flexible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to verify the proposed algorithm.

Original language	English
Pages (from-to)	485-492
Number of pages	8
Journal	Neurocomputing
Volume	57
Issue number	1-4
DOIs	https://doi.org/10.1016/j.neucom.2004.01.001
State	Published - 2004/03

Keywords

Maximum clique problem
Maximum neural network
Nonlinear self-feedback

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2004.01.001

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title = "Maximum neural network with nonlinear self-feedback for maximum clique problem",

abstract = "In this paper, by adding a nonlinear self-feedback to the maximum neural network, we propose a parallel algorithm for the maximum clique problem that introduces richer and more flexible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to verify the proposed algorithm.",

keywords = "Maximum clique problem, Maximum neural network, Nonlinear self-feedback",

author = "Jiahai Wang and Zheng Tang and Ronglong Wang",

year = "2004",

month = mar,

doi = "10.1016/j.neucom.2004.01.001",

language = "英語",

volume = "57",

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T1 - Maximum neural network with nonlinear self-feedback for maximum clique problem

AU - Wang, Jiahai

AU - Tang, Zheng

AU - Wang, Ronglong

PY - 2004/3

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N2 - In this paper, by adding a nonlinear self-feedback to the maximum neural network, we propose a parallel algorithm for the maximum clique problem that introduces richer and more flexible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to verify the proposed algorithm.

AB - In this paper, by adding a nonlinear self-feedback to the maximum neural network, we propose a parallel algorithm for the maximum clique problem that introduces richer and more flexible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to verify the proposed algorithm.

KW - Maximum clique problem

KW - Maximum neural network

KW - Nonlinear self-feedback

UR - http://www.scopus.com/inward/record.url?scp=1542471408&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2004.01.001

DO - 10.1016/j.neucom.2004.01.001

M3 - 学術論文

AN - SCOPUS:1542471408

SN - 0925-2312

VL - 57

SP - 485

EP - 492

JO - Neurocomputing

JF - Neurocomputing

IS - 1-4

ER -

Maximum neural network with nonlinear self-feedback for maximum clique problem

Abstract

Keywords

ASJC Scopus subject areas

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