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Analytical-numerical methods for hidden attractors' localization : The 16th Hilbert problem, Aizerman and Kalman conjectures, and Chua circuits. / Leonov, Gennady A.; Kuznetsov, Nikolay V.

Numerical Methods for Differential Equations, Optimization, and Technological Problems. ed. / Sergey Repin; Timo Tiihonen; Tero Tuovinen. Springer Nature, 2013. p. 41-64 (Computational Methods in Applied Sciences; Vol. 27).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Leonov, GA & Kuznetsov, NV 2013, Analytical-numerical methods for hidden attractors' localization: The 16th Hilbert problem, Aizerman and Kalman conjectures, and Chua circuits. in S Repin, T Tiihonen & T Tuovinen (eds), Numerical Methods for Differential Equations, Optimization, and Technological Problems. Computational Methods in Applied Sciences, vol. 27, Springer Nature, pp. 41-64, ECCOMAS Thematic Conference Computational Analysis and Optimization, CAO 2011, Jyvaskyla, Finland, 9/06/11. https://doi.org/10.1007/978-94-007-5288-7_3

APA

Leonov, G. A., & Kuznetsov, N. V. (2013). Analytical-numerical methods for hidden attractors' localization: The 16th Hilbert problem, Aizerman and Kalman conjectures, and Chua circuits. In S. Repin, T. Tiihonen, & T. Tuovinen (Eds.), Numerical Methods for Differential Equations, Optimization, and Technological Problems (pp. 41-64). (Computational Methods in Applied Sciences; Vol. 27). Springer Nature. https://doi.org/10.1007/978-94-007-5288-7_3

Vancouver

Leonov GA, Kuznetsov NV. Analytical-numerical methods for hidden attractors' localization: The 16th Hilbert problem, Aizerman and Kalman conjectures, and Chua circuits. In Repin S, Tiihonen T, Tuovinen T, editors, Numerical Methods for Differential Equations, Optimization, and Technological Problems. Springer Nature. 2013. p. 41-64. (Computational Methods in Applied Sciences). https://doi.org/10.1007/978-94-007-5288-7_3

Author

Leonov, Gennady A. ; Kuznetsov, Nikolay V. / Analytical-numerical methods for hidden attractors' localization : The 16th Hilbert problem, Aizerman and Kalman conjectures, and Chua circuits. Numerical Methods for Differential Equations, Optimization, and Technological Problems. editor / Sergey Repin ; Timo Tiihonen ; Tero Tuovinen. Springer Nature, 2013. pp. 41-64 (Computational Methods in Applied Sciences).

BibTeX

@inproceedings{f76508dc0b8242f9a32ddecce9da2032,
title = "Analytical-numerical methods for hidden attractors' localization: The 16th Hilbert problem, Aizerman and Kalman conjectures, and Chua circuits",
abstract = "This survey is devoted to analytical-numerical methods for hidden attractors' localization and their application to well-known problems and systems. From the computation point of view, in nonlinear dynamical systems the attractors can be regarded as self-exciting and hidden attractors. Self-exciting attractors can be localized numerically by the following standard computational procedure: after a transient process a trajectory, started from a point of an unstable manifold in a small neighborhood of unstable equilibrium, reaches an attractor and computes it. In contrast, a hidden attractor is an attractor whose basin of attraction does not contain neighborhoods of equilibria. In well-known Van der Pol, Belousov- Zhabotinsky, Lorenz, Chua, and many other dynamical systems classical attractors are self-exciting attractors and can be obtained numerically by the standard computational procedure. However, for localization of hidden attractors it is necessary to develop special analytical-numerical methods, in which at the first step the initial data are chosen in a basin of attraction and then the numerical localization (visualization) of the attractor is performed. The simplest examples of hidden attractors are internal nested limit cycles (hidden oscillations) in two-dimensional systems (see, e.g., the results concerning the second part of the 16th Hilbert's problem). Other examples of hidden oscillations are counterexamples to Aizerman's conjecture and Kalman's conjecture on absolute stability in the automatic control theory (a unique stable equilibrium coexists with a stable periodic solution in these counterexamples). In 2010, for the first time, a chaotic hidden attractor was computed first by the authors in a generalized Chua's circuit and then one chaotic hidden attractor was discovered in a classical Chua's circuit.",
author = "Leonov, {Gennady A.} and Kuznetsov, {Nikolay V.}",
note = "Publisher Copyright: {\textcopyright} 2013 Springer Science+Business Media Dordrecht.; ECCOMAS Thematic Conference Computational Analysis and Optimization, CAO 2011 ; Conference date: 09-06-2011 Through 11-06-2011",
year = "2013",
doi = "10.1007/978-94-007-5288-7_3",
language = "English",
isbn = "9789400752870",
series = "Computational Methods in Applied Sciences",
publisher = "Springer Nature",
pages = "41--64",
editor = "Sergey Repin and Timo Tiihonen and Tero Tuovinen",
booktitle = "Numerical Methods for Differential Equations, Optimization, and Technological Problems",
address = "Germany",

}

RIS

TY - GEN

T1 - Analytical-numerical methods for hidden attractors' localization

T2 - ECCOMAS Thematic Conference Computational Analysis and Optimization, CAO 2011

AU - Leonov, Gennady A.

AU - Kuznetsov, Nikolay V.

N1 - Publisher Copyright: © 2013 Springer Science+Business Media Dordrecht.

PY - 2013

Y1 - 2013

N2 - This survey is devoted to analytical-numerical methods for hidden attractors' localization and their application to well-known problems and systems. From the computation point of view, in nonlinear dynamical systems the attractors can be regarded as self-exciting and hidden attractors. Self-exciting attractors can be localized numerically by the following standard computational procedure: after a transient process a trajectory, started from a point of an unstable manifold in a small neighborhood of unstable equilibrium, reaches an attractor and computes it. In contrast, a hidden attractor is an attractor whose basin of attraction does not contain neighborhoods of equilibria. In well-known Van der Pol, Belousov- Zhabotinsky, Lorenz, Chua, and many other dynamical systems classical attractors are self-exciting attractors and can be obtained numerically by the standard computational procedure. However, for localization of hidden attractors it is necessary to develop special analytical-numerical methods, in which at the first step the initial data are chosen in a basin of attraction and then the numerical localization (visualization) of the attractor is performed. The simplest examples of hidden attractors are internal nested limit cycles (hidden oscillations) in two-dimensional systems (see, e.g., the results concerning the second part of the 16th Hilbert's problem). Other examples of hidden oscillations are counterexamples to Aizerman's conjecture and Kalman's conjecture on absolute stability in the automatic control theory (a unique stable equilibrium coexists with a stable periodic solution in these counterexamples). In 2010, for the first time, a chaotic hidden attractor was computed first by the authors in a generalized Chua's circuit and then one chaotic hidden attractor was discovered in a classical Chua's circuit.

AB - This survey is devoted to analytical-numerical methods for hidden attractors' localization and their application to well-known problems and systems. From the computation point of view, in nonlinear dynamical systems the attractors can be regarded as self-exciting and hidden attractors. Self-exciting attractors can be localized numerically by the following standard computational procedure: after a transient process a trajectory, started from a point of an unstable manifold in a small neighborhood of unstable equilibrium, reaches an attractor and computes it. In contrast, a hidden attractor is an attractor whose basin of attraction does not contain neighborhoods of equilibria. In well-known Van der Pol, Belousov- Zhabotinsky, Lorenz, Chua, and many other dynamical systems classical attractors are self-exciting attractors and can be obtained numerically by the standard computational procedure. However, for localization of hidden attractors it is necessary to develop special analytical-numerical methods, in which at the first step the initial data are chosen in a basin of attraction and then the numerical localization (visualization) of the attractor is performed. The simplest examples of hidden attractors are internal nested limit cycles (hidden oscillations) in two-dimensional systems (see, e.g., the results concerning the second part of the 16th Hilbert's problem). Other examples of hidden oscillations are counterexamples to Aizerman's conjecture and Kalman's conjecture on absolute stability in the automatic control theory (a unique stable equilibrium coexists with a stable periodic solution in these counterexamples). In 2010, for the first time, a chaotic hidden attractor was computed first by the authors in a generalized Chua's circuit and then one chaotic hidden attractor was discovered in a classical Chua's circuit.

UR - http://www.scopus.com/inward/record.url?scp=84964265507&partnerID=8YFLogxK

U2 - 10.1007/978-94-007-5288-7_3

DO - 10.1007/978-94-007-5288-7_3

M3 - Conference contribution

AN - SCOPUS:84964265507

SN - 9789400752870

T3 - Computational Methods in Applied Sciences

SP - 41

EP - 64

BT - Numerical Methods for Differential Equations, Optimization, and Technological Problems

A2 - Repin, Sergey

A2 - Tiihonen, Timo

A2 - Tuovinen, Tero

PB - Springer Nature

Y2 - 9 June 2011 through 11 June 2011

ER -

ID: 95267833