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This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently exciting that…

Systems and Control · Electrical Eng. & Systems 2021-12-07 Biqiang Mu , Tianshi Chen , Changming Cheng , Er-Wei Bai

We report the experimental evidence of the existence of a random attractor in a fully developed turbulent swirling flow. By defining a global observable which tracks the asymmetry in the flux of angular momentum imparted to the flow, we can…

In the paper, several concrete examples, as well as their numerical simulations, are given to show that parameter identification based on the so-called adaptive synchronization techniques might be failed if those functions with parameters…

Adaptation and Self-Organizing Systems · Physics 2009-11-13 Wei Lin , Huan-fei Ma

This paper proposes an Extended-Kalman-Filter-like observer for parameter estimation during synchronization of chaotic systems. The exponential stability of the observer is guaranteed by a persistent excitation condition. This approach is…

Chaotic Dynamics · Physics 2017-06-21 L. Torres

Systems with delayed feedback can possess chaotic attractors with extremely high dimension, even if only a few physical degrees of freedom are involved. We propose a state space reconstruction from time series data of a scalar observable,…

chao-dyn · Physics 2019-08-17 Rainer Hegger , Martin J. Bünner , Holger Kantz , Antonino Giaquinta

Many dynamical systems described by nonlinear ODEs are unstable. Their associated solutions do not converge towards an equilibrium point, but rather converge towards some invariant subset of the state space called an attractor set. For a…

Dynamical Systems · Mathematics 2023-05-05 Morgan Jones , Matthew M. Peet

We use the cross correlation sum introduced recently by H. Kantz to study symmetry properties of chaotic attractors. In particular, we apply it to a system of six coupled nonlinear oscillators which was shown by Kroon et al. to have…

chao-dyn · Physics 2009-10-28 Peter Schneider , Peter Grassberger

In this paper, we characterize the synchronization phenomenon of hyperchaotic scalar non-linear delay dynamics in a fully-developed chaos regime. Our results rely on the observation that, in that regime, the stationary statistical…

Chaotic Dynamics · Physics 2008-10-08 Adrian A. Budini

The phenomenon of Stochastic Resonance (SR) is observed in a completely deterministic setting - with thermal noise being replaced by one-dimensional chaos. The piecewise linear map investigated in the paper shows a transition from…

chao-dyn · Physics 2009-10-31 Sitabhra Sinha , Bikas K. Chakrabarti

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost…

Optimization and Control · Mathematics 2014-10-17 Stefan Streif , Matthias Karl , Ali Mesbah

In the hidden Markov process, there is a possibility that two different transition matrices for hidden and observed variables yield the same stochastic behavior for the observed variables. Since such two transition matrices cannot be…

Statistics Theory · Mathematics 2024-09-10 Masahito Hayashi

Using the method of quantum trajectories we study a quantum chaotic dissipative ratchet appearing for particles in a pulsed asymmetric potential in the presence of a dissipative environment. The system is characterized by directed transport…

Statistical Mechanics · Physics 2007-05-23 Gabriel G. Carlo , Giuliano Benenti , Giulio Casati , Dima L. Shepelyansky

Group zero-attracting LMS and its reweighted form have been proposed for addressing system identification problems with structural group sparsity in the parameters to estimate. Both algorithms however suffer from a trade-off between…

Signal Processing · Electrical Eng. & Systems 2018-04-02 Danqi Jin , Jie Chen , Cedric Richard , Jingdong Chen

We use recent advances in the machine learning area known as 'reservoir computing' to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of…

Chaotic Dynamics · Physics 2018-01-17 Jaideep Pathak , Zhixin Lu , Brian R. Hunt , Michelle Girvan , Edward Ott

Certain deterministic non-linear systems may show chaotic behaviour. Time series derived from such systems seem stochastic when analyzed with linear techniques. However, uncovering the deterministic structure is important because it allows…

chao-dyn · Physics 2008-02-03 Dimitris Kugiumtzis , Bjoern Lillekjendlie , Nils Christophersen

The dynamics of unidirectionally coupled chaotic Lorenz systems is investigated. It is revealed that chaos is present in the response system regardless of generalized synchronization. The presence of sensitivity is theoretically proved, and…

Chaotic Dynamics · Physics 2016-10-10 Mehmet Onur Fen

The Nikolaevskiy model for pattern formation with continuous symmetry exhibits spatiotemporal chaos with strong scale separation. Extensive numerical investigations of the chaotic attractor reveal unexpected scaling behavior of the…

Chaotic Dynamics · Physics 2010-06-02 Ralf W. Wittenberg , Ka-Fai Poon

We consider chaotic dynamics of a system of two coupled ring resonators with a linear gain and a nonlinear absorption. Such a structure can be implemented in various settings including microresonator nanostructures, polariton condensates,…

We discuss algorithms for combining sequential prediction strategies, a task which can be viewed as a natural generalisation of the concept of universal coding. We describe a graphical language based on Hidden Markov Models for defining…

Information Theory · Computer Science 2013-11-27 Wouter M. Koolen , Steven de Rooij

The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, chaotic systems can be repeatedly measured to produce arbitrarily-detailed information about the underlying…

Machine Learning · Computer Science 2023-01-31 William Gilpin