Parlitz, Ulrich

[["dc.bibliographiccitation.firstpage","073123"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Chaos: An Interdisciplinary Journal of Nonlinear Science"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Hegedűs, F."],["dc.contributor.author","Krähling, P."],["dc.contributor.author","Aron, M."],["dc.contributor.author","Lauterborn, W."],["dc.contributor.author","Mettin, R."],["dc.contributor.author","Parlitz, U."],["dc.date.accessioned","2021-04-14T08:25:38Z"],["dc.date.available","2021-04-14T08:25:38Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1063/5.0005424"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81694"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1089-7682"],["dc.relation.issn","1054-1500"],["dc.title","Feedforward attractor targeting for non-linear oscillators using a dual-frequency driving technique"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","036209"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PHYSICAL REVIEW E"],["dc.bibliographiccitation.volume","86"],["dc.contributor.author","Suetani, Hiromichi"],["dc.contributor.author","Soejima, Karin"],["dc.contributor.author","Matsuoka, Rei"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Hata, Hiroki"],["dc.date.accessioned","2018-11-07T09:05:51Z"],["dc.date.available","2018-11-07T09:05:51Z"],["dc.date.issued","2012"],["dc.description.abstract","Dripping water from a faucet is a typical example exhibiting rich nonlinear phenomena. For such a system, the time stamps at which water drops separate from the faucet can be directly observed in real experiments, and the time series of intervals tau(n) between drop separations becomes a subject of analysis. Even if the mass m(n) of a drop at the onset of the nth separation, which is difficult to observe experimentally, exhibits perfectly deterministic dynamics, it may be difficult to obtain the same information about the underlying dynamics from the time series tau(n). This is because the return plot tau(n-1) vs. tau(n) may become a multivalued relation (i.e., it doesn't represent a function describing deterministic dynamics). In this paper, we propose a method to construct a nonlinear coordinate which provides a \"surrogate\" of the internal state m(n) from the time series of tau(n). Here, a key of the proposed approach is to use ISOMAP, which is a well-known method of manifold learning. We first apply it to the time series of tau(n) generated from the numerical simulation of a phenomenological mass-spring model for the dripping faucet system. It is shown that a clear one-dimensional map is obtained by the proposed approach, whose characteristic quantities such as the Lyapunov exponent, the topological entropy, and the time correlation function coincide with the original dripping faucet system. Furthermore, we also analyze data obtained from real dripping faucet experiments, which also provide promising results."],["dc.identifier.doi","10.1103/PhysRevE.86.036209"],["dc.identifier.isi","000308873100003"],["dc.identifier.pmid","23030999"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25418"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","2470-0053"],["dc.relation.issn","2470-0045"],["dc.title","Manifold learning approach for chaos in the dripping faucet"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","093701"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Chaos: An Interdisciplinary Journal of Nonlinear Science"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Cherry, Elizabeth M."],["dc.contributor.author","Fenton, Flavio H."],["dc.contributor.author","Krogh-Madsen, Trine"],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2020-12-10T18:12:37Z"],["dc.date.available","2020-12-10T18:12:37Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1063/1.5003940"],["dc.identifier.eissn","1089-7682"],["dc.identifier.issn","1054-1500"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74442"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Introduction to Focus Issue: Complex Cardiac Dynamics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","23"],["dc.bibliographiccitation.journal","Neural Networks"],["dc.bibliographiccitation.lastpage","29"],["dc.bibliographiccitation.volume","115"],["dc.contributor.author","Thiede, Luca Anthony"],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2020-12-10T15:20:27Z"],["dc.date.available","2020-12-10T15:20:27Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.neunet.2019.02.001"],["dc.identifier.issn","0893-6080"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72671"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Gradient based hyperparameter optimization in Echo State Networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","20140093"],["dc.bibliographiccitation.issue","2034"],["dc.bibliographiccitation.journal","Philosophical Transactions of the Royal Society of London. A, Mathematical, Physical and Engineering Sciences"],["dc.bibliographiccitation.volume","373"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Luther, S."],["dc.contributor.author","Wessel, N."],["dc.contributor.author","Penzel, Thomas"],["dc.date.accessioned","2018-11-07T10:00:56Z"],["dc.date.available","2018-11-07T10:00:56Z"],["dc.date.issued","2015"],["dc.description.abstract","Transition patterns between different sleep stages are analysed in terms of probability distributions of symbolic sequences for young and old subjects with and without sleep disorder. Changes of these patterns due to ageing are compared with variations of transition probabilities due to sleep disorder."],["dc.identifier.doi","10.1098/rsta.2014.0093"],["dc.identifier.isi","000346962100006"],["dc.identifier.pmid","25548271"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37911"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/120"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.issn","1471-2962"],["dc.relation.issn","1364-503X"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.title","Changes of sleep-stage transitions due to ageing and sleep disorder"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","062916"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PHYSICAL REVIEW E"],["dc.bibliographiccitation.volume","90"],["dc.contributor.author","Rey, Daniel"],["dc.contributor.author","Eldridge, Michael"],["dc.contributor.author","Morone, Uriel"],["dc.contributor.author","Abarbanel, Henry D. I."],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Schumann-Bischoff, Jan"],["dc.date.accessioned","2018-11-07T09:31:08Z"],["dc.date.available","2018-11-07T09:31:08Z"],["dc.date.issued","2014"],["dc.description.abstract","Information in measurements of a nonlinear dynamical system can be transferred to a quantitative model of the observed system to establish its fixed parameters and unobserved state variables. After this learning period is complete, one may predict the model response to new forces and, when successful, these predictions will match additional observations. This adjustment process encounters problems when the model is nonlinear and chaotic because dynamical instability impedes the transfer of information from the data to the model when the number of measurements at each observation time is insufficient. We discuss the use of information in the waveform of the data, realized through a time delayed collection of measurements, to provide additional stability and accuracy to this search procedure. Several examples are explored, including a few familiar nonlinear dynamical systems and small networks of Colpitts oscillators."],["dc.identifier.doi","10.1103/PhysRevE.90.062916"],["dc.identifier.isi","000346833500012"],["dc.identifier.pmid","25615173"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/31473"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","1550-2376"],["dc.relation.issn","1539-3755"],["dc.title","Using waveform information in nonlinear data assimilation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","056217"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PHYSICAL REVIEW E"],["dc.bibliographiccitation.volume","70"],["dc.contributor.author","Langer, G."],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2018-11-07T10:44:28Z"],["dc.date.available","2018-11-07T10:44:28Z"],["dc.date.issued","2004"],["dc.description.abstract","Two approaches for modeling of parameter dependence of dynamical systems from time series are investigated and applied to different examples. For both methods it is assumed that a few Lime series are available that have been measured for different (known) parameter values of the underlying (experimental) dynamical System. The objective is to model the changing dynamics of the system as a function of its parameters and to use this for experimental bifurcation analysis. Using parametrized families the tasks of modeling the dynamics and of modeling its parameter dependence are separated. Technical difficulties that may occur with this approach are discussed and illustrated. An alternative are extended state space models where both modeling, tasks are treated simultaneously. To obtain reliable models from a few time series only, ensembles of models are employed that show very good extrapolation and generalization properties."],["dc.identifier.doi","10.1103/PhysRevE.70.056217"],["dc.identifier.isi","000225970700065"],["dc.identifier.pmid","15600738"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/47273"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","2470-0053"],["dc.relation.issn","2470-0045"],["dc.title","Modeling parameter dependence from time series"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","016206"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","PHYSICAL REVIEW E"],["dc.bibliographiccitation.volume","72"],["dc.contributor.author","Ahlborn, A."],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2018-11-07T09:38:20Z"],["dc.date.available","2018-11-07T09:38:20Z"],["dc.date.issued","2005"],["dc.description.abstract","Multiple delay feedback control (MDFC) with two, three, or four different and independent delay times is used to stabilize steady states of various chaotic dynamical systems. A comparison with delayed feedback control methods that are based on a single (fundamental) delay time [Pyragas' time delay auto synchronization (TDAS) and extended TDAS] shows that MDFC is more effective for fixed point stabilization in terms of stability and flexibility, in particular for large delay times."],["dc.identifier.doi","10.1103/PhysRevE.72.016206"],["dc.identifier.isi","000230887100043"],["dc.identifier.pmid","16090065"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33046"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","American Physical Soc"],["dc.relation.issn","1539-3755"],["dc.title","Controlling dynamical systems using multiple delay feedback control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","157"],["dc.bibliographiccitation.lastpage","158"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Zwirnmann, Henning"],["dc.contributor.author","Zabel, Markus"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Luther, Stefan"],["dc.date.accessioned","2019-02-26T15:08:20Z"],["dc.date.available","2019-02-26T15:08:20Z"],["dc.date.issued","2014"],["dc.description.abstract","We evaluate several machine learning algorithms in the context of long-term prediction of cardiac diseases. Results from applying K Nearest Neighbors Classifiers (KNN), Support Vector Machines (SVM) and Random Forests (RF) to data from a cardiological long-term study suggests that multivariate methods can significantly improve classification results. SVMs were found to yield the best results in Matthews Correlation Coefficient and are most stable with respect to a varying number of features."],["dc.identifier.doi","10.1109/ESGCO.2014.6847567"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57623"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/53"],["dc.language.iso","en"],["dc.notes.status","fcwi"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway, NJ"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.conference","8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)"],["dc.relation.eventend","2014-05-28"],["dc.relation.eventlocation","Trento, Italy"],["dc.relation.eventstart","2014-05-25"],["dc.relation.isbn","978-1-4799-3969-5"],["dc.relation.isbn","978-1-4799-3968-8"],["dc.relation.isbn","978-1-4799-3970-1"],["dc.relation.ispartof","2014 8th Conference of the European Study Group on Cardiovascoular Oscillations (ESGCO 2014)"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.title","Evaluation of Machine Learning Methods for the Long-Term Prediction of Cardiac Diseases"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","727"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Nonlinear Science"],["dc.bibliographiccitation.lastpage","762"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Kuptsov, Pavel V."],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2018-11-07T09:05:26Z"],["dc.date.available","2018-11-07T09:05:26Z"],["dc.date.issued","2012"],["dc.description.abstract","Lyapunov exponents are well-known characteristic numbers that describe growth rates of perturbations applied to a trajectory of a dynamical system in different state space directions. Covariant (or characteristic) Lyapunov vectors indicate these directions. Though the concept of these vectors has been known for a long time, they became practically computable only recently due to algorithms suggested by Ginelli et al. [Phys. Rev. Lett. 99, 2007, 130601] and by Wolfe and Samelson [Tellus 59A, 2007, 355]. In view of the great interest in covariant Lyapunov vectors and their wide range of potential applications, in this article we summarize the available information related to Lyapunov vectors and provide a detailed explanation of both the theoretical basics and numerical algorithms. We introduce the notion of adjoint covariant Lyapunov vectors. The angles between these vectors and the original covariant vectors are norm-independent and can be considered as characteristic numbers. Moreover, we present and study in detail an improved approach for computing covariant Lyapunov vectors. Also we describe how one can test for hyperbolicity of chaotic dynamics without explicitly computing covariant vectors."],["dc.description.sponsorship","European Community [HEALTH-F2-2009-241526]; RFBR-DFG [08-02-91963]"],["dc.identifier.doi","10.1007/s00332-012-9126-5"],["dc.identifier.isi","000309230000004"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25314"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0938-8974"],["dc.title","Theory and Computation of Covariant Lyapunov Vectors"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]