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dc.contributor.authorSima, Jiri
dc.contributor.authorOrponen, Pekka
dc.date.accessioned2018-07-18T06:48:58Z
dc.date.available2018-07-18T06:48:58Z
dc.date.issued2001fi
dc.identifier.citationSima, J., & Orponen, P. (2001). Exponential transients in continuous-time symmetric Hopfield nets. In G. Dorffner, H. Bischof, & K. Hornik (Eds.), <em>ICANN 2001 : Artificial Neural Networks. Proceedings of the International Conference Vienna, Austria, August 21-25, 2001</em> (pp. 806-813). Lecture Notes in Computer Science, 2130. Berlin: Springer-Verlag. <a href="https://doi.org/10.1007/3-540-44668-0_112">doi:10.1007/3-540-44668-0_112</a>fi
dc.identifier.otherTUTKAID_6446
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/58975
dc.description.abstractWe establish a fundamental result in the theory of continuous-time neural computation, by showing that so called continuous-time symmetric Hopfield nets, whose asymptotic convergence is always guaranteed by the existence of a Liapunov function may, in the worst case, possess a transient period that is exponential in the network size. The result stands in contrast to e.g. the use of such network models in combinatorial optimization applications.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer-Verlag
dc.relation.ispartofICANN 2001 : Artificial Neural Networks. Proceedings of the International Conference Vienna, Austria, August 21-25, 2001
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.otherneural networksfi
dc.subject.otherdynamical systemsfi
dc.subject.otherHopfield netsfi
dc.subject.otherstabilityfi
dc.titleExponential transients in continuous-time symmetric Hopfield netsfi
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201807173589
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-07-17T09:15:13Z
dc.relation.isbn978-3-540-42486-4
dc.description.reviewstatuspeerReviewed
dc.format.pagerange806-813
dc.relation.numberinseries2130
dc.type.versionacceptedVersion
dc.rights.copyright© Springer-Verlag Berlin Heidelberg 2001
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Artificial Neural Networks
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/3-540-44668-0_112


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