An analytical approach to single node delay-coupled reservoir computing
DC Element | Wert | Sprache |
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dc.contributor.author | Schumacher, J. | |
dc.contributor.author | Toutounji, H. | |
dc.contributor.author | Pipa, G. | |
dc.date.accessioned | 2021-12-23T16:30:26Z | - |
dc.date.available | 2021-12-23T16:30:26Z | - |
dc.date.issued | 2013 | |
dc.identifier.isbn | 9783642407277 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/16582 | - |
dc.description | Conference of 23rd International Conference on Artificial Neural Networks, ICANN 2013 ; Conference Date: 10 September 2013 Through 13 September 2013; Conference Code:99717 | |
dc.description.abstract | Reservoir computing has been successfully applied in difficult time series prediction tasks by injecting an input signal into a spatially extended reservoir of nonlinear subunits to perform history-dependent nonlinear computation. Recently, the network was replaced by a single nonlinear node, delay-coupled to itself. Instead of a spatial topology, subunits are arrayed in time along one delay span of the system. As a result, the reservoir exists only implicitly in a single delay differential equation, numerical solving of which is costly. We derive here approximate analytical equations for the reservoir by solving the underlying system explicitly. The analytical approximation represents the system accurately and yields comparable performance in reservoir benchmark tasks, while reducing computational costs by several orders of magnitude. This has important implications with respect to electronic realizations of the reservoir and opens up new possibilities for optimization and theoretical investigation. © 2013 Springer-Verlag Berlin Heidelberg. | |
dc.language.iso | en | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Analytical approximation | |
dc.subject | Approximate analytical | |
dc.subject | Computational costs | |
dc.subject | Delay differential equations | |
dc.subject | Nonlinear computations | |
dc.subject | Orders of magnitude | |
dc.subject | Theoretical investigations | |
dc.subject | Time series prediction, Benchmarking | |
dc.subject | Differential equations, Neural networks | |
dc.title | An analytical approach to single node delay-coupled reservoir computing | |
dc.type | conference paper | |
dc.identifier.doi | 10.1007/978-3-642-40728-4_4 | |
dc.identifier.scopus | 2-s2.0-84884922913 | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884922913&doi=10.1007%2f978-3-642-40728-4_4&partnerID=40&md5=10a26854cc4cdcb1bdf53eeeaa977be2 | |
dc.description.volume | 8131 LNCS | |
dc.description.startpage | 26 | |
dc.description.endpage | 33 | |
dc.publisher.place | Sofia | |
dcterms.isPartOf.abbreviation | Lect. Notes Comput. Sci. | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PiGo340 | - |
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geprüft am 05.05.2024