about
Improved false nearest neighbor method to detect determinism in time series dataUntangling Brain-Wide Dynamics in Consciousness by Cross-EmbeddingAttracting dynamics of frontal cortex ensembles during memory-guided decision-makingNon-linear analysis indicates chaotic dynamics and reduced resilience in model-based Daphnia populations exposed to environmental stressDistinguishing time-delayed causal interactions using convergent cross mappingPredicting influenza with dynamical methodsDelay differential analysis of time seriesBrainNetVis: an open-access tool to effectively quantify and visualize brain networks.Systematic determination of order parameters for chain dynamics using diffusion maps.Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variabilitySensor-independent stimulus representations.Predicting climate effects on Pacific sardineNonlinear behavior of the tarka flute's distinctive sounds.Reconstructing embedding spaces of coupled dynamical systems from multivariate data.Identifying physical properties of a CO2 laser by dynamical modeling of measured time series.Markov models from data by simple nonlinear time series predictors in delay embedding spaces.Recurrence plots of experimental data: To embed or not to embed?On noise reduction methods for chaotic data.Don't bleach chaotic data.Noise in chaotic data: Diagnosis and treatment.Using waveform information in nonlinear data assimilation.Effects of data windows on the methods of surrogate data.Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex.Forecasting critical transitions using data-driven nonstationary dynamical modeling.Reconstructing state spaces from multivariate data using variable delays.Dynamics from noisy data with extreme timing uncertainty.Delay-coordinates embeddings as a data mining tool for denoising speech signals.Predicting phase synchronization in a spiking chaotic CO2 laser.Noise-induced chaos in an optically injected semiconductor laser modelPredicting and characterizing data sequences from structure-variable systems.Controlling the unstable steady state in a multimode laser.Modeling and synchronizing chaotic systems from time-series data.Noise reduction in chaotic time-series data: A survey of common methods.Nonlinear noise reduction: A case study on experimental data.Local false nearest neighbors and dynamical dimensions from observed chaotic data.Fitting ordinary differential equations to chaotic data.The nonlinear predictability of the electrotelluric field variations data analyzed with support vector machines as an earthquake precursor.Detecting synchronizations in an asymmetric vocal fold model from time series data.From phase space to frequency domain: a time-frequency analysis for chaotic time series.Modeling dynamics from only output data.
P2860
Q21686226-5E1CD5D0-67C9-4F95-AFA4-459651F1AB10Q27317313-35A9EE9A-628A-4D05-A67C-BA16362A47B2Q27333501-B890C3C0-A909-4A86-91CC-BE590FDE1A4FQ28538605-7A24F680-9EA5-4C58-8418-AE1AF4AB0A49Q28606945-AF9FE487-45AF-457F-BE32-CA23F36365BBQ30394256-0389B542-99F7-4802-8C3F-C31DEC4215F2Q30416009-87354A41-A3C7-4762-A4C5-26FE687FC869Q30484913-1766A07D-A862-4170-B0AF-B52B19F7FF29Q30496093-DED7AE3E-2C0D-44BB-BBD1-1A6DA3A8E4C3Q30505948-8636866C-3F73-4A87-A371-2113614008F5Q30531519-3934EEB2-3645-4949-88CF-DF9092C83CF1Q30539047-54D063BD-3969-4807-B1C4-55749CE56297Q30571810-0F4BBFFC-1754-48DF-96C9-9A3E68599CAFQ30681479-449383F7-FBF5-4C5E-B858-2549BAF37C95Q30690043-4612E92E-E855-4F0A-83BF-CC84ACEFA02AQ30697605-CB58AF7B-1F91-46E0-8615-05751C271699Q30799359-5D202CD0-47E1-4C01-A6F5-35B32CC2436BQ30799388-AEF6C0CE-2A3F-418A-9671-069AF73E1529Q30799397-F05A4291-DC11-4544-989E-7925AB5380E3Q30799505-055E9724-D4AE-4B1D-AE7F-46F08D67D937Q30885723-8E218C07-9339-4B12-921F-39B451D80E37Q30998940-F6F9D961-4E75-482A-A9CD-42CDF930B652Q31036415-BDAE6D6F-F0C6-43B9-87D0-79F95DB0AD14Q31036803-C0503C83-FD54-4943-8CBC-2346F4F4236BQ31065056-9178F6BA-7D07-49AB-AB18-FCD8E1EED38FQ31086253-381682BE-EE31-477A-AD5E-FA6CA8A2D726Q31089438-DBD24074-0C9E-4668-897D-BF39AEAB94FCQ31125087-0366F368-2688-4967-84D7-411E8BF00EF1Q31814667-84900461-8F78-4CA4-9F85-B918EE8A5754Q31911112-ED0F11CC-324F-477E-B876-7B4401188CF4Q31911147-A9F15C05-0410-4997-AB33-64866C36A759Q31911645-BD4AE70D-3E9D-40BE-A9F9-2584567DF72FQ31912008-DED744B1-A2D6-4838-974B-E90532449428Q31912024-CC5CBF2B-E0D6-462A-8774-5B2E26F31A83Q31912156-2FD6B1CA-64BE-4AA4-8435-048BB0D1A8D6Q31926189-45F4EFE8-6D92-4841-BC6C-EC770141A364Q33195580-EEFFDF1A-9303-471C-80D9-DC5BC8A2B45EQ33214136-1E035356-020E-4F21-83B7-EBBAA0A62119Q33293198-026453DE-CE3A-45F2-A68E-DD98E0572E31Q33466321-2B541425-F002-46F2-A2DD-537D2321D624
P2860
description
im November 1991 veröffentlichter wissenschaftlicher Artikel
@de
wetenschappelijk artikel
@nl
наукова стаття, опублікована в листопаді 1991
@uk
name
Embedology
@en
Embedology
@nl
type
label
Embedology
@en
Embedology
@nl
prefLabel
Embedology
@en
Embedology
@nl
P2093
P356
P1476
Embedology
@en
P2093
James A. Yorke
Martin Casdagli
P2888
P304
P356
10.1007/BF01053745
P577
1991-11-01T00:00:00Z