Analysis of a Generalized Lorenz–Stenflo Equation

Zhang, Fuchen; Chen, Rui; Chen, Xiusu

doi:https://doi.org/10.1155/2017/7520590

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Research Article | Open Access

Volume 2017 | Article ID 7520590 | https://doi.org/10.1155/2017/7520590

Analysis of a Generalized Lorenz–Stenflo Equation

Fuchen Zhang,^1,2Rui Chen ,³and Xiusu Chen¹

Academic Editor: Giacomo Innocenti

Received20 Sept 2017

Accepted26 Nov 2017

Published13 Dec 2017

Abstract

Although the globally attractive sets of a hyperchaotic system have important applications in the fields of engineering, science, and technology, it is often a difficult task for the researchers to obtain the globally attractive set of the hyperchaotic systems due to the complexity of the hyperchaotic systems. Therefore, we will study the globally attractive set of a generalized hyperchaotic Lorenz–Stenflo system describing the evolution of finite amplitude acoustic gravity waves in a rotating atmosphere in this paper. Based on Lyapunov-like functional approach combining some simple inequalities, we derive the globally attractive set of this system with its parameters. The effectiveness of the proposed methods is illustrated via numerical examples.

1. Introduction

In 1963, Lorenz found the well-known three-dimensional Lorenz model when he studied the dynamics of the atmosphere [1]. Since then, various complex dynamical behaviors of the Lorenz system have been studied by mathematicians, physicists, and engineers from various fields due to various applications in the fields of engineering, science, and technology [2–14]. In order to improve the stability or predictability of the Lorenz system, Stenflo and Leonov derived the following four-dimensional Lorenz–Stenflo system with four parameters to describe the dynamics of the atmosphere [15, 16]:

In order to give a better description of the atmosphere, Chen and Liang propose a generalized Lorenz–Stenflo system with six parameters according to the Lorenz–Stenflo system [17]:where , , , and are state variables and , , , , , and are positive parameters of system (2). System (2) can describe the dynamic behavior of finite amplitude acoustic gravity waves in a rotating atmosphere.

The Lyapunov exponents of the dynamical system (2) are calculated numerically for the parameter values , , , , , and with the initial state System (2) has Lyapunov exponents as , , , and for the parameters listed above (see [18, 19] for a detailed discussion of Lyapunov exponents of strange attractors in dynamical systems). Thus, system (2) has two positive Lyapunov exponents and the strange attractor, which means system (2) can exhibit a variety of interesting and complex chaotic behaviors. System (2) has a hyperchaotic attractor with , , , , , and , as shown in Figures 1–4.

In this paper, all the simulations are carried out by using fourth-order Runge-Kutta Method with time-step

The rest of this paper is organized as follows. In Section 2, the globally attractive set for the chaotic attractors in (2) is studied using Lyapunov stability theory. To validate the ultimate bound estimation, numerical simulations are also provided. Finally, the conclusions are drawn in Section 3.

2. Bounds for the Chaotic Attractors in System (2)

Theorem 1. For any , , , , , , there exists a positive number , such thatis the ultimate bound set of system (2), where

Proof. Define the following Lyapunov-like function:where , and , are arbitrary constants.
And we can getLet Then, we can get the surface :is an ellipsoid in for Outside , , while inside , Thus, the ultimate boundedness for system (2) can only be reached on Since the Lyapunov-like function is a continuous function and is a bounded closed set, then the function (4) can reach its maximum value on the surface that is defined in (6). Obviously, contains solutions of system (2). It is obvious that the set is the ultimate bound set for system (2).
This completes the proof.

Theorem 2. Suppose that
Let be an arbitrary solution of system (2) and Then the estimationholds for system (2), and thus is the globally exponential attractive set of system (2); that is,

Proof. Define the following functions:then we can getConstruct the Lyapunov-like functionDifferentiating the above Lyapunov-like function in (11) with respect to time along the trajectory of system (2) yieldsThus, we havewhich clearly shows that is the globally exponential attractive set of system (2).
The proof is complete.

Remark 3. (i) In particular, let us take , in Theorem 2, we can get the conclusions below.

Suppose that

Let be an arbitrary solution of system (2) and Then the estimationholds for system (2), and thus is the globally exponential attractive set and positive invariant set of system (2); that is,

(ii) Let us take ; then we can getas the globally exponential attractive set and positive invariant set of system (2) according to Theorem 2.

When , we can get that as the globally exponential attractive set and positive invariant set of system (2).

Figure 5 shows hyperchaotic attractor of system (2) in the space defined by . Figure 6 shows hyperchaotic attractor of system (2) in the space defined by . Figure 7 shows hyperchaotic attractor of system (2) in the space defined by . Figure 8 shows hyperchaotic attractor of system (2) in the space defined by .

3. Conclusions

In this paper, we have investigated some global dynamics of a generalized Lorenz–Stenflo system describing the evolution of finite amplitude acoustic gravity waves in a rotating atmosphere. Based on the Lyapunov method, the globally attractive sets were formulated combining simple inequalities. Finally, numerical examples were presented to show the effectiveness of the proposed method.

Disclosure

All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant no. 11501064), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant no. KJ1500605), the Research Fund of Chongqing Technology and Business University (Grant no. 2014-56-11), China Postdoctoral Science Foundation (Grant no. 2016M590850), Chongqing Postdoctoral Science Foundation Special Funded Project (Grant no. Xm2017174), and the Research Fund of Chongqing Technology and Business University (Grant no. 1752073). The authors thank Professors Jinhu Lu in Institute of Systems Science, Chinese Academy of Sciences, Gaoxiang Yang in Ankang University, Ping Zhou in Chongqing University of Posts and Telecommunications, and Min Xiao in Nanjing University of Posts and Telecommunications for their help.

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Copyright

Copyright © 2017 Fuchen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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