Paper Info
Title
An improved competitive Hopfield network with inhibitive competitive activation mechanism for maximum clique problem
Abstract
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.
Year
DOI
Venue
2014
10.1016/j.neucom.2012.11.055
Neurocomputing
Keywords
DocType
Volume
maximum clique problem,inhibitive competitive activation mechanism,discrete competitive Hopfield network,intermediate level,competitive dynamic,initial value,weights definition,flexible weight definition method,benchmark problem,improved competitive Hopfield network
Journal
130,
ISSN
Citations 
PageRank 
0925-2312
0
0.34
References 
Authors
9
4
Name
Order
Citations
PageRank
Gang Yang1329.38
Nan Yang281.55
Junyan Yi3113.40
Zheng Tang421.25