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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25026
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dc.contributor.authorSenn, Christoph Walter-
dc.contributor.authorKumazawa, Itsuo-
dc.date.accessioned2022-05-20T15:11:55Z-
dc.date.available2022-05-20T15:11:55Z-
dc.date.issued2022-03-10-
dc.identifier.issn2673-2688de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25026-
dc.description.abstractNoise of any kind can be an issue when translating results from simulations to the real world. We suddenly have to deal with building tolerances, faulty sensors, or just noisy sensor readings. This is especially evident in systems with many free parameters, such as the ones used in physical reservoir computing. By abstracting away these kinds of noise sources using intervals, we derive a regularized training regime for reservoir computing using sets of possible reservoir states. Numerical simulations are used to show the effectiveness of our approach against different sources of errors that can appear in real-world scenarios and compare them with standard approaches. Our results support the application of interval arithmetics to improve the robustness of mass-spring networks trained in simulations.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofAIde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectReservoir computingde_CH
dc.subjectEcho state networkde_CH
dc.subjectRecurrent neural networkde_CH
dc.subjectSimulationde_CH
dc.subjectRobustnessde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleAbstract reservoir computingde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.3390/ai3010012de_CH
dc.identifier.doi10.21256/zhaw-25026-
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end210de_CH
zhaw.pages.start194de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume3de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.monitoring.costperiod2022de_CH
Appears in collections:Publikationen School of Engineering

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Senn, C. W., & Kumazawa, I. (2022). Abstract reservoir computing. Ai, 3(1), 194–210. https://doi.org/10.3390/ai3010012
Senn, C.W. and Kumazawa, I. (2022) ‘Abstract reservoir computing’, AI, 3(1), pp. 194–210. Available at: https://doi.org/10.3390/ai3010012.
C. W. Senn and I. Kumazawa, “Abstract reservoir computing,” AI, vol. 3, no. 1, pp. 194–210, Mar. 2022, doi: 10.3390/ai3010012.
SENN, Christoph Walter und Itsuo KUMAZAWA, 2022. Abstract reservoir computing. AI. 10 März 2022. Bd. 3, Nr. 1, S. 194–210. DOI 10.3390/ai3010012
Senn, Christoph Walter, and Itsuo Kumazawa. 2022. “Abstract Reservoir Computing.” Ai 3 (1): 194–210. https://doi.org/10.3390/ai3010012.
Senn, Christoph Walter, and Itsuo Kumazawa. “Abstract Reservoir Computing.” Ai, vol. 3, no. 1, Mar. 2022, pp. 194–210, https://doi.org/10.3390/ai3010012.


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