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首页> 外文期刊>International journal of bifurcation and chaos in applied sciences and engineering >Numerical Analysis and Improved Algorithms for Lyapunov-Exponent Calculation of Discrete-Time Chaotic Systems
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Numerical Analysis and Improved Algorithms for Lyapunov-Exponent Calculation of Discrete-Time Chaotic Systems

机译:离散混沌系统李雅普诺夫指数计算的数值分析和改进算法

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摘要

Lyapunov exponent is an important index for describing chaotic systems behavior, and the largest Lyapunov exponent can be used to determine whether a system is chaotic or not. For discrete-time dynamical systems, the Lyapunov exponents are calculated by an eigenvalue method. In theory, according to eigenvalue method, the more accurate calculations of Lyapunov exponent can be obtained with the increment of iterations, and the limits also exist. However, due to the finite precision of computer and other reasons, the results will be numeric overflow, unrecognized, or inaccurate, which can be stated as follows: (1) The iterations cannot be too large, otherwise, the simulation result will appear as an error message of NaN or Inf; (2) If the error message of NaN or Inf does not appear, then with the increment of iterations, all Lyapunov exponents will get close to the largest Lyapunov exponent, which leads to inaccurate calculation results; (3) From the viewpoint of numerical calculation, obviously, if the iterations are too small, then the results are also inaccurate. Based on the analysis of Lyapunov-exponent calculation in discrete-time systems, this paper investigates two improved algorithms via QR orthogonal decomposition and SVD orthogonal decomposition approaches so as to solve the above-mentioned problems. Finally, some examples are given to illustrate the feasibility and effectiveness of the improved algorithms.
机译:李雅普诺夫指数是描述混沌系统行为的重要指标,最大的李雅普诺夫指数可用于确定系统是否混沌。对于离散时间动力系统,李雅普诺夫指数通过特征值方法计算。从理论上讲,根据特征值方法,随着迭代次数的增加,可以获得更精确的李雅普诺夫指数计算,并且存在极限。但是,由于计算机的精度有限以及其他原因,结果将是数值溢出,无法识别或不准确,可以表述为:(1)迭代不能太大,否则,模拟结果将显示为NaN或Inf的错误消息; (2)如果没有出现NaN或Inf的错误消息,则随着迭代次数的增加,所有Lyapunov指数将接近最大Lyapunov指数,从而导致计算结果不准确; (3)从数值计算的角度来看,显然,如果迭代次数太小,则结果也不准确。在分析离散时间系统的李雅普诺夫指数计算的基础上,研究了两种改进的QR正交分解和SVD正交分解方法,以解决上述问题。最后,通过一些例子说明了改进算法的可行性和有效性。

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