4.6 Article

Simple estimation method for the largest Lyapunov exponent of continuous fractional-order differential equations

出版社

ELSEVIER
DOI: 10.1016/j.physa.2020.125478

关键词

Largest Lyapunov exponent; Fractional-order chaotic equations; Chaos

资金

  1. National Natural Science Foundation of China [61672124]
  2. Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund [MMJJ20170203]
  3. Liaoning Province Science and Technology Innovation Leading Talents Program Project [XLYC1802013]
  4. Key RAMP
  5. D Projects of Liaoning Province [2019020105-JH2/103]
  6. Jinan City' 20 universities' Funding Projects Introducing Innovation Team Program [2019GXRC031]
  7. Science and Technology Research Program of Chongqing Municipal Education Commission [KJ1703056, KJQN201900529]
  8. Research Project of Chongqing Normal University [17XLB001, 16XYY21]

向作者/读者索取更多资源

This paper presents a simple method based on perturbing the initial value to directly estimate the largest Lyapunov exponent. The method reduces parameter errors and is feasible and easy to implement without computing the Jacobian matrix and phase space.
In this paper, a simple method based on the perturbation of the initial value is presented to directly estimate the largest Lyapunov exponent (LLE) from continuous fractional-order differential equations. Two nearby trajectories are used to directly compute the LLE and reduce parameter errors. Another initial value is obtained by perturbing the given initial value. Two solutions are then developed from a fractional-order chaotic system by using the two initial values. The evolutionary distance between the two solutions is calculated, and the LLE is determined from the curve of the track distance. Some continuous fractional-order chaotic and nonchaotic differential equations are applied to verify the effectiveness of our method. Experimental results indicate that the proposed method is feasible and easy to implement instead of computing the Jacobian matrix and phase space. (C) 2020 Elsevier B.V. All rights reserved.

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