An improved competitive Hopfield network with inhibitive competitive activation mechanism for maximum clique problem

被引：3

作者：

Yang, Gang ^{[1
]}

Yang, Nan ^{[1
]}

Yi, Junyan ^{[2
,3
]}

Tang, Zheng ^{[4
]}

机构：

[1] Renmin Univ China, Informat Sch, Beijing, Peoples R China

[2] Zhejiang Univ Technol, Hangzhou, Zhejiang, Peoples R China

[3] Beijing Univ Civil Engn & Architecture, Beijing, Peoples R China

[4] Toyama Univ, Fac Engn, Toyama 930, Japan

来源：

NEUROCOMPUTING | 2014年 / 130卷

基金：

中国国家自然科学基金;

关键词：

Competitive Hopfield network; Maximum clique problem; Inhibitive competitive mechanism; Community detection; NEURAL-NETWORK; COMBINATORIAL OPTIMIZATION; COMMUNITY; ALGORITHM; MODEL;

D O I：

10.1016/j.neucom.2012.11.055

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 paper, we analyze the formula of weights definition in the discrete competitive Hopfield network (DCHOM) and point out its flaw when using it to solve some special instances of maximum clique problem (MCP). Based on the analysis, we propose an improved competitive Hopfield network algorithm (ICHN). In ICHN, we introduce a flexible weight definition method which excites the competitive dynamics, and we also present an initial values setting strategy which efficiently increases the probability of finding optimal solutions. Furthermore, an inhibitive competitive activation mechanism is introduced to form a new input updating rule which reduces significantly the number of neurons with an intermediate level of activations. Our algorithm effectively overcomes the flaw of the DCHOM, and exhibits powerful solving ability for the MCP. Experiments on the benchmark problems and practical applications verify the validity of our algorithm. (C) 2013 Published by Elsevier B.V.

引用

页码：28 / 35

页数：8

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