Data Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networks

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dc.date.accessioned2023-04-19T12:01:55Z
dc.date.available2023-04-19T12:01:55Z
dc.date.issued2023-03-24
dc.description.sponsorshipGefördert im Rahmen des Projekts DEALger
dc.identifierdoi:10.17170/kobra-202304197837
dc.identifier.urihttp://hdl.handle.net/123456789/14597
dc.language.isoeng
dc.relation.doidoi:10.1002/pamm.202200318
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.ddc510
dc.subject.ddc620
dc.subject.swdNeuronales Netzger
dc.subject.swdDuffing-Oszillatorger
dc.subject.swdNichtlineare Schwingungger
dc.titleData Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networkseng
dc.typeAufsatz
dc.type.versionpublishedVersion
dcterms.abstractIn this work, we propose a novel approach to the data-driven prediction of vibration responses of nonlinear systems. The main idea is based on Autoregressive Neural Networks (ARNN) to model the nonlinear transfer behaviour between an external excitation and the system response. We propose an autoregressive network architecture with embedded symmetry using bias-free tanh activation and guarantee Input-to-State-Stability (ISS) by enforcing a special penalty term to the weights. The resulting training procedure is analysed for the example of a DUFFING oscillator with white noise excitation. In a BAYESian optimisation, it is found that beyond enforcing input-to-state-stability, the stabilising penalty term also decreases sensitivity with respect to other training parameters compared to other classical techniques. Furthermore, we show that the stabilised ARNN is able to give excellent approximations of the nonlinear response of the DUFFING oscillator for a wide range of excitation intensities. In contrast, linear models, such as autoregressive models with exogenous input (ARX) in time domain or linear transfer functions in frequency domain, will only find some linear approximation. In particular, by construction, they will not be able to capture nonlinear effects for arbitrary amplitudes and excitation levels.eng
dcterms.accessRightsopen access
dcterms.creatorWestmeier, Tobias
dcterms.creatorKreuter, Daniel
dcterms.creatorBäuerle, Simon
dcterms.creatorHetzler, Hartmut
dcterms.extent6 Seiten
dcterms.source.articlenumbere202200318
dcterms.source.identifiereissn:1617-7061
dcterms.source.issueIssue 1
dcterms.source.journalProceedings in applied mathematics and mechanics : PAMMeng
dcterms.source.volumeVolume 22
kup.iskupfalse

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