Phase space reconstruction and estimation of the largest Lyapunov exponent for gait kinematic data

2015 ◽  
Author(s):  
Henryk Josiński ◽  
Adam Świtoński ◽  
Agnieszka Michalczuk ◽  
Konrad Wojciechowski
Keyword(s):  
Phase Space ◽  
Lyapunov Exponent ◽  
Phase Space Reconstruction ◽  
Largest Lyapunov Exponent ◽  
Kinematic Data ◽  
The Largest Lyapunov Exponent

Chaotic Calculation and Prediction on Volume Deformation of MgO Concrete Based on Lyapunov Exponent

2010 ◽  
Vol 34-35 ◽  
pp. 446-450
Author(s):  
Jun Wei Song ◽  
Xiao Ping Cai
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Lyapunov Exponent ◽  
Interpolation Method ◽  
Phase Space Reconstruction ◽  
Near Neighbor ◽  
Largest Lyapunov Exponent ◽  
Volume Deformation ◽  
Measured Deformation ◽  
The Largest Lyapunov Exponent

For the measured deformation time series of concrete engineering, linear and nonlinear chaotic prediction methods are represented for the autogenously volume deformation of concrete on basis of phase space reconstruction. Thus，the chaotic analytic method is set up for the prediction of the measured deformation time series of concrete engineering. At first，the autogenously volume deformation of RCC mixed with MgO are simply presented. Then，for the measured convergent deformation time series of RCC mixed with MgO，the interpolation method is adopted and an equal interval deformation time series is obtained. The results show that the largest Lyapunov exponent is 0.02 based on phase space reconstruction. Finally，it is separately predicted by the method of equidistance in near neighbor and that of the largest Lyapunov exponent prediction，and the predicted deformation values are ideal compared with the measured deformation values.

DETECTING THE STATE OF THE DUFFING OSCILLATOR BY PHASE SPACE TRAJECTORY AUTOCORRELATION

2013 ◽  
Vol 23 (04) ◽  
pp. 1350065 ◽  
Author(s):  
VAHID RASHTCHI ◽  
MOHSEN NOURAZAR
Keyword(s):  
Phase Space ◽  
Lyapunov Exponent ◽  
Duffing Oscillator ◽  
Simple Calculation ◽  
The State ◽  
Detection Methods ◽  
Largest Lyapunov Exponent ◽  
Chaotic Oscillator ◽  
The Largest Lyapunov Exponent ◽  
Phase Space Trajectory

Detecting the state of the Duffing oscillator, a type of well-known chaotic oscillator, deeply affects the accuracy of its application. Considering this, the present paper introduced a novel method for detecting the state of the Duffing oscillator. Binary outputs, simple calculation, high precision and fast response time were the main advantages of the phase space trajectory autocorrelation. Also, this study explained the largest Lyapunov exponent as well as a number of other methods commonly employed in detecting the state of the Duffing oscillator. The precision and effectiveness of the method introduced was compared with other well-known state detection methods such as the 0-1 test and the largest Lyapunov exponent.

scholarly journals The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Abigail Ortiz ◽  
Kamil Bradler ◽  
Maxine Mowete ◽  
Stephane MacLean ◽  
Julie Garnham ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Fractal Dimension ◽  
Lyapunov Exponent ◽  
Detrended Fluctuation Analysis ◽  
Largest Lyapunov Exponent ◽  
Fluctuation Analysis ◽  
Mood Regulation ◽  
The Largest Lyapunov Exponent ◽  
Depressive Episodes ◽  
Detrended Fluctuation

Abstract Background Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. Results There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension. Conclusions The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short.

Estimation of the Largest Lyapunov Exponent of Discontinuous Systems Using Chaos Synchronization

1999 ◽  
Author(s):  
Andrzej Stefanski ◽  
Jerzy Wojewoda ◽  
Tomasz Kapitaniak ◽  
John Brindley
Keyword(s):  
Lyapunov Exponent ◽  
Mechanical System ◽  
Dry Friction ◽  
Chaos Synchronization ◽  
Largest Lyapunov Exponent ◽  
Discontinuous Systems ◽  
System A ◽  
The Largest Lyapunov Exponent

Abstract Properties of chaos synchronization have been used for estimation of the largest Lyapunov exponent of a discontinuous mechanical system. A method for such estimation is proposed and an example is shown, based on coupling of two identical systems with dry friction which is modelled according to the Popp-Stelter formula.

Automatic identification of eye movements using the largest lyapunov exponent

2018 ◽  
Vol 41 ◽  
pp. 10-20 ◽  
Author(s):  
Alexandra I. Korda ◽  
Pantelis A. Asvestas ◽  
George K. Matsopoulos ◽  
Errikos M. Ventouras ◽  
Nikolaos Smyrnis
Keyword(s):  
Eye Movements ◽  
Lyapunov Exponent ◽  
Largest Lyapunov Exponent ◽  
Automatic Identification ◽  
The Largest Lyapunov Exponent

Nonlinear dynamics of forced transitional jets: periodic and chaotic attractors

1994 ◽  
Vol 263 ◽  
pp. 93-132 ◽  
Author(s):  
George Broze ◽  
Fazle Hussain
Keyword(s):  
Phase Diagram ◽  
Phase Space ◽  
Lyapunov Exponent ◽  
Conceptual Model ◽  
Chaotic Attractor ◽  
Excitation Frequency ◽  
Chaotic Attractors ◽  
Largest Lyapunov Exponent ◽  
Open Flow ◽  
Vortex Pairing

Conclusive experimental evidence is presented for the existence of a low-dimensional temporal dynamical system in an open flow, namely the near field of an axisymmetric, subsonic free jet. An initially laminar jet (4 cm air jet in the Reynolds number range 1.1 × 104 [Lt ] ReD × 9.1 × 104) with a top-hat profile was studied using single-frequency, longitudinal, bulk excitation. Two non-dimensional control parameters – forcing frequency StD (≡fexD/Ue, where fez is the excitation frequency, D is the jet exit diameter and Ue is the exit velocity) and forcing amplitude af (≡ u’f/Ue, where u’f is the jet exit r.m.s. longitudinal velocity fluctuation at the excitation frequency) – were varied over the ranges 10-4 < af < 0.3 and 0.3 < StD < 3.0 in order to construct a phase diagram. Periodic and chaotic states were found over large domains of the parameter space. The periodic attractors correspond to stable pairing (SP) and stable double pairing (SDP) of rolled-up vortices. One chaotic attractor, near SP in the parameter space, results from nearly periodic modulations of pairing (NPMP) of vortices. At large scales (i.e. approximately the size of the attractor) in phase space, NPMP exhibits approximately quasi-periodic behaviour, including modulation sidebands around ½fex in u-spectra, large closed loops in its Poincaré sections, correlation dimension v ∼ 2 and largest Lyapunov exponent λ1 ∼ 0. But investigations at smaller scales (i.e. distances greater than, but of the order of, trajectory separation) in phase space reveal chaos, as shown by v > 2 and λ1 > 0. The other chaotic attractor, near SDP, results from nearly periodic modulations of the first vortex pairing but chaotic modulations of the second pairing and has a broadband spectrum, a dimension 2.5 [Lt ] v [Lt ] 3 and the largest Lyapunov exponent 0.2 [Lt ] λ1 [Lt ] 0.7 bits per orbit (depending on measurement locations in physical and parameter spaces).A definition that distinguishes between physically and dynamically open flows is proposed and justified by our experimental results. The most important conclusion of this study is that a physically open flow, even one that is apparently dynamically open due to convective instability, can exhibit dynamically closed behaviour as a result of feedback. A conceptual model for transitional jets is proposed based on twodimensional instabilities, subharmonic resonance and feedback from downstream vortical structures to the nozzle lip. Feedback was quantified and shown to affect the exit fundamental–subharmonic phase difference ϕ – a crucial variable in subharmonic resonance and, hence, vortex pairing. The effect of feedback, the sensitivity of pairings to ϕ, the phase diagram, and the documented periodic and chaotic attractors demonstrate the validity of the proposed conceptual model.

Dynamics of Human Spine in Jumps Using Lyapunov Exponents

2009 ◽  
Author(s):  
Eliza A. Banu ◽  
Dan Marghitu ◽  
P. K. Raju
Keyword(s):  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Largest Lyapunov Exponent ◽  
Kinematic Data ◽  
Finite Time Lyapunov Exponents ◽  
Human Spine ◽  
Chaotic Nature ◽  
Male Subjects ◽  
Lyapunov Exponent Spectrum

Characterizing and quantifying the local dynamics of the human spine during various athletic exercises is important for therapeutic and physiotherapy reasons or athletic related effects on the human spine. In this study we computed the largest finite-time Lyapunov exponents for the spine of a human subject during an athletic exercise for several volunteers. The kinematic data was collected using the 3D motion capture system (Motion Realty Inc.). Four healthy male subjects were asked to perform two sets of 30 jumps. In order to draw a conclusion about the chaotic nature of the dynamics of the lumbar spine Lyapunov exponent spectrum was calculated for each volunteer which included four Lyapunov exponents, one negative, one very close to zero and two positive. Subjects 1, 2 and 4 registered a significant increase in the largest Lyapunov exponent from the first set jumps to the next. Subject 3 registered no change in the values of Lyapunov exponents. Nonlinear analysis of the human spine demonstrates the chaotic nature of the system. The computation of the largest Lyapunov exponents enables a more precise characterization of the dynamics of the human spine.

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