EMAN : equivalent mass attraction network

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dc.contributor.authorErdem Mahmut H.
dc.contributor.authorBaskomurcu Gamze
dc.contributor.authorOzturk Yusuf
dc.date.accessioned2019-10-27T00:33:25Z
dc.date.available2019-10-27T00:33:25Z
dc.date.issued1994
dc.departmentEge Üniversitesien_US
dc.descriptionIEEEen_US
dc.descriptionProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) -- 27 June 1994 through 29 June 1994 -- Orlando, FL, USA -- 42367en_US
dc.description.abstractThis paper introduces a new neural network model for binary pattern classification. In a recent work we have proposed a network model, namely, MAN (Mass Attraction Network) [1,2] which can be used as an autoassociator. In MAN, memory items have been considered as masses at the corners of a hypercube. Exploiting Newton's mass attraction theory, a recall scheme utilizing 'attraction forces' between memory items and input patterns has been developed. EMAN is the consequence of efforts to extent the concept to do classification. The main idea in EMAN is to create an equivalent mass instead of two close masses. After introducing MAN and EMAN concepts, some improvements will be presented. This paper concludes with simulation results.en_US
dc.identifier.endpage1108en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1103en_US
dc.identifier.urihttps://hdl.handle.net/11454/24052
dc.identifier.volume2en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE, Piscataway, NJ, United Statesen_US
dc.relation.ispartofIEEE International Conference on Neural Networks - Conference Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleEMAN : equivalent mass attraction networken_US
dc.typeConference Objecten_US

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