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Extending the Power and Capacity of Constraint Satisfaction Networks

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Permanent URL	http://hdl.lib.byu.edu/1877/2422
Title	Extending the Power and Capacity of Constraint Satisfaction Networks
BYU Creator	Martinez, Tony R. Zeng, Xinchuan
Keywords	Hopfield network; activation function; relaxation procedure
Description/Abstract	This work focuses on improving the Hopfield network for solving optimization problems. Although much work has been done in this area, the performance of the Hopfield network is still not satisfactory in terms of valid convergence and quality of solutions. We address this issue in this work by combing a new activation function (EBA) and a new relaxation procedure (CR) in order to improve the performance of the Hopfield network. Each of EBA and CR has been individually demonstrated capable of substantially improving the performance. The combined approach has been evaluated through 20,000 simulations based on 200 randomly generated city distributions of the 10-city traveling salesman problem. The result shows that combining the two methods is able to further improve the performance. Compared to CR without combining with EBA, the combined approach increases the percentage of valid tours by 21.0% and decreases the error rate by 46.4%. As compared to the original Hopfield method (using neither EBA nor CR), the combined approach increases the percentage of valid tours by 245.7% and decreases the error rate by 64.1%.
Source	Zeng, X. and Martinez, T. R., "Extending the Power and Capacity of Constraint Satisfaction Networks", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99, CD paper #190, 1999.;
Publisher	IEEE
Date Original	1999
Date Digital	2010-09-09
Language	English eng en
Publication Type	Peer-Reviewed Articles Conference Proceedings
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.;
Copyright Use Information	http://www.lib.byu.edu/generic_copyright.html

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