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dc.contributor.advisorLouri, Ahmeden_US
dc.contributor.authorLee, Hyuk-Jae, 1965-
dc.creatorLee, Hyuk-Jae, 1965-en_US
dc.date.accessioned2013-04-03T13:09:28Z
dc.date.available2013-04-03T13:09:28Z
dc.date.issued1991en_US
dc.identifier.urihttp://hdl.handle.net/10150/278008
dc.description.abstractIn this thesis we consider an efficient cooling schedule for a mean field annealing (MFA) algorithm. We combine the MFA algorithm with microcanonical simulation (MCS) method and propose a new algorithm called the microcanonical mean field annealing (MCMFA) algorithm. In the proposed algorithm, the cooling speed is controlled by the current temperature so that the amount of computation in MFA can be reduced without a degradation of performance. Unlike that produced by MFA, the solution quality produced by MCMFA is not affected by the choice of the initial temperature. Properties of MCMFA are analyzed and simulated with Hopfield neural networks (HNN). In order to compare MCMFA with MFA, we apply both algorithms to three problems namely, the graph bipartitioning problem, the traveling salesman problem and the weighted matching problem. Simulation results show that MCMFA produces a superior performance to that of MFA.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectComputer Science.en_US
dc.titleAn efficient cooling algorithm for annealed neural networks with applications to optimization problemsen_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
dc.identifier.proquest1346426en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.nameM.S.en_US
dc.identifier.bibrecord.b27226700en_US
refterms.dateFOA2018-08-13T12:50:13Z
html.description.abstractIn this thesis we consider an efficient cooling schedule for a mean field annealing (MFA) algorithm. We combine the MFA algorithm with microcanonical simulation (MCS) method and propose a new algorithm called the microcanonical mean field annealing (MCMFA) algorithm. In the proposed algorithm, the cooling speed is controlled by the current temperature so that the amount of computation in MFA can be reduced without a degradation of performance. Unlike that produced by MFA, the solution quality produced by MCMFA is not affected by the choice of the initial temperature. Properties of MCMFA are analyzed and simulated with Hopfield neural networks (HNN). In order to compare MCMFA with MFA, we apply both algorithms to three problems namely, the graph bipartitioning problem, the traveling salesman problem and the weighted matching problem. Simulation results show that MCMFA produces a superior performance to that of MFA.


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