Exponential transients in continuous-time symmetric Hopfield nets
| dc.contributor.author | Sima, Jiri | |
| dc.contributor.author | Orponen, Pekka | |
| dc.contributor.editor | Dorffner, Georg | |
| dc.contributor.editor | Bischof, Horst | |
| dc.contributor.editor | Hornik, Kurt | |
| dc.date.accessioned | 2018-07-18T06:48:58Z | |
| dc.date.available | 2018-07-18T06:48:58Z | |
| dc.date.issued | 2001 | |
| dc.identifier.citation | Sima, J., & Orponen, P. (2001). Exponential transients in continuous-time symmetric Hopfield nets. In G. Dorffner, H. Bischof, & K. Hornik (Eds.), <i>ICANN 2001 : Artificial Neural Networks. Proceedings of the International Conference Vienna, Austria, August 21-25, 2001</i> (pp. 806-813). Springer-Verlag. Lecture Notes in Computer Science, 2130. <a href="https://doi.org/10.1007/3-540-44668-0_112" target="_blank">https://doi.org/10.1007/3-540-44668-0_112</a> | |
| dc.identifier.other | CONVID_24419310 | |
| dc.identifier.other | TUTKAID_6446 | |
| dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/58975 | |
| dc.description.abstract | We 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.mimetype | application/pdf | |
| dc.language.iso | eng | |
| dc.publisher | Springer-Verlag | |
| dc.relation.ispartof | ICANN 2001 : Artificial Neural Networks. Proceedings of the International Conference Vienna, Austria, August 21-25, 2001 | |
| dc.relation.ispartofseries | Lecture Notes in Computer Science | |
| dc.rights | In Copyright | |
| dc.subject.other | neural networks | |
| dc.subject.other | Hopfield nets | |
| dc.subject.other | stability | |
| dc.title | Exponential transients in continuous-time symmetric Hopfield nets | |
| dc.type | conferenceObject | |
| dc.identifier.urn | URN:NBN:fi:jyu-201807173589 | |
| dc.contributor.laitos | Matematiikan ja tilastotieteen laitos | fi |
| dc.contributor.laitos | Department of Mathematics and Statistics | en |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
| dc.date.updated | 2018-07-17T09:15:13Z | |
| dc.relation.isbn | 978-3-540-42486-4 | |
| dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
| dc.description.reviewstatus | peerReviewed | |
| dc.format.pagerange | 806-813 | |
| dc.relation.numberinseries | 2130 | |
| dc.type.version | acceptedVersion | |
| dc.rights.copyright | © Springer-Verlag Berlin Heidelberg 2001 | |
| dc.rights.accesslevel | openAccess | fi |
| dc.relation.conference | International Conference on Artificial Neural Networks | |
| dc.subject.yso | dynaamiset systeemit | |
| dc.format.content | fulltext | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p38899 | |
| dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
| dc.relation.doi | 10.1007/3-540-44668-0_112 | |
| dc.type.okm | A4 |
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