Quantifying Chaos by Various Computational Methods. Part 1: Simple Systems

Author:

Awrejcewicz J.,Krysko A.V.,Erofeev N.P.,Dobriyan V.,Barulina M.A.,Krysko V.A.

Abstract

The first part of the paper was aimed at analyzing the given nonlinear problem by different methods of computation of the Lyapunov exponents (Wolf method [1], Rosenstein method [2], Kantz method [3], method based on the modification of a neural network [4, 5], and the synchronization method [6, 7]) for the classical problems governed by difference and differential equations (Hénon map [8], hyper-chaotic Hénon map [9], logistic map [10], Rössler attractor [11], Lorenz attractor [12]) and with the use of both Fourier spectra and Gauss wavelets [13]. It was shown that a modification of the neural network method [4, 5] makes it possible to compute a spectrum of Lyapunov exponents, and then to detect a transition of the system regular dynamics into chaos, hyper-chaos, hyper hyper-chaos and deep chaos [14-16]. Different algorithms for computation of Lyapunov exponents were validated by comparison with the known dynamical systems spectra of the Lyapunov exponents. The carried out analysis helps comparatively estimate the employed methods in order to choose the most suitable/optimal one to study different kinds of dynamical systems and different classes of problems in both this and the next paper parts.

Publisher

MDPI AG

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Classification of EEG Signals in Two Levels of Normal and Anxious Using Nonlinear Features;Journal of Cognitive Psychology;2021-10-01

2. Chaotic Oscillations Type of Flexible Closed Cylindrical Nanoshells Under the Strip Loads Action;Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2;2020-12-17

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