 Research
 Open Access
 Published:
Chaotification for linear delay difference equations
Advances in Difference Equations volume 2013, Article number: 59 (2013)
Abstract
This paper is concerned with chaotification of linear delay difference equations via the feedback control technique. The controlled system is first reformulated into a linear discrete dynamical system. Then, a chaotification theorem based on the snapback repeller theory for maps is established. The controlled system is proved to be chaotic in the sense of both Devaney an LiYorke. An illustrative example is provided with computer simulations.
MSC:34C28, 37D45, 74H65.
1 Introduction
Chaotification (or called anticontrol of chaos) is a process that makes a nonchaotic system chaotic, or enhances a chaotic system to present stronger or different type of chaos. In recent years, it has been found that chaos can actually be useful under some circumstances, for example, in human brain analysis [1, 2], heartbeat regulation [3, 4], encryption [5], digital communications [6], etc. So, sometimes it is useful and even important to make a system chaotic or create new types of chaos. It has attracted increasing interest in research on chaotification of dynamical systems due to the great potentials of chaos in many nontraditional applications.
In the pursuit of chaotifying discrete dynamical systems, a simple yet mathematically rigorous chaotification method was first developed by Chen and Lai [7–9] from a feedback control approach. They showed that the Lyapunov exponents of a controlled system are positive [7], and the controlled system via the modoperation is chaotic in the sense of Devaney when the original system is linear and is chaotic in a weaker sense of Wiggins when the original system is nonlinear [9]. Later, Wang and Chen [10] further showed that the ChenLai algorithm for chaotification also leads to chaos in the sense of LiYorke. This method plays an important role in studying chaotification problems of discrete dynamical systems. Recently, Shi and Chen [11] studied chaotification for some discrete dynamical systems governed by continuous maps and showed that the controlled systems are chaotic in the sense of both Devaney and LiYorke. It is noticed that all the above chaotification problems for discrete dynamical systems are formulated in finitedimensional real spaces. More recently, Shi et al. [12] first studied chaotification for discrete dynamical systems in Banach spaces via the feedback control technique. They showed that some controlled systems in finitedimensional real spaces studied by [9] and [13] are chaotic in the sense of Devaney as well as in the sense of both LiYorke and Wiggins. Particularly, they extended and proved the Marotto theorem to general Banach spaces [14], established some chaotification theorems for discrete dynamical systems in (infinitedimensional) Banach spaces [12], and showed the controlled systems are chaotic in the sense of both Devaney and LiYorke. The reader is referred to Chen and Shi [15] for a survey of chaotification of discrete dynamical systems and some references cited therein.
Time delay arises in many realistic systems with feedback in science and engineering. The delay difference equations have been studied by many researchers. Although there exist some general chaotification schemes for finitedimensional or infinitedimensional discrete dynamical systems as stated in the above, there are few results on the chaotification of linear delay difference equations. In this paper, we employ the ChenLai method, from the feedback control approach to study chaotification of linear delay difference equations and prove that the controlled system is chaotic in the sense of both Devaney and LiYorke by applying the snapback repeller theory; see [14, 16, 17] for the theory.
The rest of the paper is organized as follows. In Section 2, the chaotification problem under investigation is described, and some concepts, one lemma, and reformulation of the controlled system are introduced. In Section 3, the chaotification problem is studied and a chaotification criterion is established. Finally, we provide an example to illustrate the theoretical result with computer simulations in Section 4.
2 Preliminaries
In this section, we describe the chaotification problem, give a reformulation of the linear delay difference equation, and introduce some fundamental concepts and a criterion of chaos, which will be used in the next section.
2.1 Description of chaotification problem
Consider the following linear delay difference equation:
where $k\ge 1$ is a fixed integer and ${a}_{j}$, $1\le j\le k+1$, are constant parameters.
The object is to design a simple control input sequence $\{v(n)\}$ such that the output of the controlled system
is chaotic in the sense of both Devaney and LiYorke (see Definitions 1 and 2 below). The controller to be designed in this paper is in the form of
where β and ${\gamma}_{j}$, $1\le j\le k+1$, are undetermined parameters.
2.2 Reformulation
In this subsection, we reformulate equations (1) and (2) into $k+1$dimensional discrete dynamical systems. By setting
equation (1) and the controlled system (2) with controller (3) can be written as the following discrete systems on ${\mathbf{R}}^{k+1}$:
respectively, where $u={({u}_{1},{u}_{2},\dots ,{u}_{k+1})}^{T},b={(0,\dots ,0,1)}^{T}\in {\mathbf{R}}^{k+1}$,
$v(n)=\beta sin({\sum}_{j=1}^{k+1}{\gamma}_{j}{u}_{j}(n))\in \mathbf{R}$, and the map $F:{\mathbf{R}}^{k+1}\to {\mathbf{R}}^{k+1}$ is given by
Systems (4) and (5) are said to be induced by equations (1) and (2), respectively. It is easy to see that the dynamical behaviors of equations (1) and (2) are the same as those of their induced systems (4) and (5), respectively.
2.3 Some concepts and a criterion of chaos
Since Li and Yorke [18] first introduced a precise mathematical definition of chaos, there appeared several different definitions of chaos, some are stronger and some are weaker, depending on the requirements in different problems; see [19–22]etc. For convenience, we list two definitions of chaos in the sense of LiYorke and Devaney, which are used in the paper.
Definition 1 Let $(X,d)$ be a metric space, $f:X\to X$ be a map, and S be a set of X with at least two distinct points. Then S is called a scrambled set of f if for any two distinct points $x,y\in S$,
The map f is said to be chaotic in the sense of LiYorke if there exists an uncountable scrambled set S of f.
Definition 2 [19]
Let $(X,d)$ be a metric space. A map $f:V\subset X\to V$ is said to be chaotic on V in the sense of Devaney if

(i)
the set of the periodic points of f is dense in V;

(ii)
f is topologically transitive in V;

(iii)
f has sensitive dependence on initial conditions in V.
In 1992, Banks et al. [23] proved that conditions (i) and (ii) together imply condition (iii) if f is continuous in V. So, condition (iii) is redundant in the above definition in this case. It has been proved in [24] that under some conditions, chaos in the sense of Devaney is stronger than that in the sense of LiYorke.
Now, we introduce some relative concepts for system (2), which are motivated by [[12], Definitions 5.1 and 5.2].
Definition 3

(i)
A point $x\in {\mathbf{R}}^{k+1}$ is called an mperiodic point of system (2) if $x\in {\mathbf{R}}^{k+1}$ is an mperiodic point of its induced system (5), that is, ${F}^{m}(x)=x$, ${F}^{j}(x)\ne x$, $1\le j\le m1$. In the special case of $m=1$, x is called a fixed point or a steady state of system (2).

(ii)
The concepts of density of periodic points, topological transitivity, sensitive dependence on initial conditions, and the invariant set for system (2) are defined similarly to those for its induced system (5) in ${\mathbf{R}}^{k+1}$.

(iii)
System (2) is said to be chaotic in the sense of Devaney (or LiYorke) on $V\subset {\mathbf{R}}^{k+1}$ if its induced system (5) is chaotic in the sense of Devaney (or LiYorke) on $V\subset {\mathbf{R}}^{k+1}$.
In this paper, we will use the criterion of chaos established by Shi et al. to chaotify the linear delay difference equation (1). For convenience, we state it as follows.
Lemma 1 ([[12], Theorem 2.1], [[14], Theorem 4.4])
Let $f:{\mathbf{R}}^{n}\to {\mathbf{R}}^{n}$ be a map with a fixed point $z\in {\mathbf{R}}^{n}$. Assume that

(i)
f is continuously differentiable in a neighborhood of z and all the eigenvalues of $Df(z)$ have absolute values larger than 1, which implies that there exist a positive constant r and a norm $\parallel \cdot \parallel $ in ${\mathbf{R}}^{n}$ such that f is expanding in ${\overline{B}}_{r}(z)$ in $\parallel \cdot \parallel $, where ${\overline{B}}_{r}(z)$ is the closed ball of radius r centered at z in $({\mathbf{R}}^{n},\parallel \cdot \parallel )$;

(ii)
z is a snapback repeller of f with ${f}^{m}({x}_{0})=z$, ${x}_{0}\ne z$, for some ${x}_{0}\in {B}_{r}(z)$ and some positive integer m, where ${B}_{r}(z)$ is the open ball of radius r centered at z in $({\mathbf{R}}^{n},\parallel \cdot \parallel )$. Furthermore, f is continuously differentiable in some neighborhoods of ${x}_{0},{x}_{1},\dots ,{x}_{m1}$, respectively, and $detDf({x}_{j})\ne 0$ for $0\le j\le m1$, where ${x}_{j}=f({x}_{j1})$ for $1\le j\le m1$.
Then for each neighborhood U of z, there exist a positive integer $k>m$ and a Cantor set $\mathrm{\Lambda}\subset U$ such that ${f}^{k}:\mathrm{\Lambda}\to \mathrm{\Lambda}$ is topologically conjugate to the symbolic dynamical system $\sigma :{\sum}_{2}^{+}\to {\sum}_{2}^{+}$. Consequently, there exists a compact and perfect invariant set $V\subset {\mathbf{R}}^{n}$, containing the Cantor set Λ, such that f is chaotic on V in the sense of Devaney as well as in the sense of LiYorke, and has a dense orbit in V.
Remark 1 Lemma 1 extends and improves the Marroto theorem [17]. Under the conditions of Lemma 1, z is a regular and nondegenerate snapback repeller. Therefore, Lemma 1 can be briefly stated as follows: ‘a regular and nondegenerate snapback repeller in ${\mathbf{R}}^{n}$ implies chaos in the sense of both Devaney and LiYorke’. We refer to [12, 14] for details.
3 Chaotification for linear delay difference equations
In this section, we will show that the controlled system (5) with a singleinput state feedback controller (3), i.e., the controlled system (2) with controller (3), is chaotic in the sense of both Devaney and LiYorke for some parameters β and ${\gamma}_{j}$, $1\le j\le k$.
Consider the fixed points of system (5). It is obvious that $O:={(0,\dots ,0)}^{T}\in {\mathbf{R}}^{k+1}$ is always a fixed point, and other fixed points $P:={({x}_{0},\dots ,{x}_{0})}^{T}\in {\mathbf{R}}^{k+1}$ satisfy the following equation:
In the following, we only show the fixed point O can be a regular and nondegenerate snapback repeller of system (5) under some conditions.
Theorem 1 There exist some constants $\beta >0$ and ${\gamma}_{j}$, $1\le j\le k$, with
such that the controlled system (5), and consequently system (2), is chaotic in the sense of both Devaney and LiYorke.
Proof We will use Lemma 1 to prove this theorem. So, it suffices to show that all the conditions in Lemma 1 are satisfied. As assumed in the statement of the theorem, let β and ${\gamma}_{1}$ satisfy condition (7) throughout the proof.
First, we will show that O is an expanding fixed point of F in some norm in ${\mathbf{R}}^{k+1}$. In fact, F is continuously differentiable in ${\mathbf{R}}^{k+1}$, and the Jacobian matrix of F at O is
Its eigenvalues are determined by
It follows from (8) and the second relation of condition (7) that all the eigenvalues of $DF(O)$ have absolute values larger than 1 in norm. Otherwise, suppose that there exists an eigenvalue ${\lambda}_{0}$ of $DF(O)$ with ${\lambda}_{0}\le 1$, then we get the following inequality:
which is a contradiction. Hence, it follows from the first condition of Lemma 1, there exist a positive constant r and a norm ${\parallel \cdot \parallel}^{\ast}$ in ${\mathbf{R}}^{k+1}$ such that O is an expanding fixed point of F in ${\overline{B}}_{r}(O)$ in the norm ${\parallel \cdot \parallel}^{\ast}$, that is,
where $\mu >1$ is an expanding coefficient of F in ${\overline{B}}_{r}(O)$ and ${\overline{B}}_{r}(O)$ is the closed ball centered at $O\in {\mathbf{R}}^{k+1}$ of radius r with respect to the norm ${\parallel \cdot \parallel}^{\ast}$.
From (6) and (7), we see that for sufficiently large β and ${\gamma}_{1}$, the absolute value of ${x}_{0}$ can become very small. So, the distance of the fixed points O and P can be very small, and consequently, the r obtained above will be less than 1.
Next, we show O is a snapback repeller of F in the norm ${\parallel \cdot \parallel}^{\ast}$. For fixed ${a}_{1}$ and sufficiently large β and ${\gamma}_{1}$, the equation
has a solution ${x}^{\ast}\ne 0$ near 0, which implies that ${O}_{0}={({x}^{\ast},0,\dots ,0)}^{T}\in {B}_{r}(O)$ with ${O}_{0}\ne O$. It follows from the first relation of (7) that $\frac{{a}_{j}}{\beta}<1$, $1\le j\le k+1$. Let
Then
Set ${O}_{j}=F({O}_{j1})$, $1\le j\le k+1$. We can easily get that ${O}_{j}={(0,\dots ,0,\underset{j}{\underset{\u23df}{1,0,\dots ,0}})}^{T}\notin {B}_{r}(O)$ for $1\le j\le k+1$, and
which implies that O is a snapback repeller of F.
Finally, we show that
A direct calculation shows that for any $u={({u}_{1},\dots ,{u}_{k+1})}^{T}\in {\mathbf{R}}^{k+1}$,
So, the following inequalities:
can be carried out from equations (9) and (10) by choosing sufficiently large β. Therefore, all the assumptions in Lemma 1 are satisfied and O is a regular and nondegenerate snapback repeller of system (5). So, system (5), i.e., equation (2), is chaotic in the sense of both Devaney and LiYorke. The proof is complete. □
Remark 2 Zhang and Chen [25], Kwok and Tang [26] had independently studied the chaotification of system (4) with the following singleinput controllers:
respectively, where $b={(0,\dots ,0,1)}^{T}\in {\mathbf{R}}^{k+1}$ and ${\alpha}_{j}$, ${\beta}_{j}$, $1\le j\le k+1$, are undetermined parameters. They showed that the controlled system is chaotic in the sense of LiYorke by using the Marotto theorem. However, there exists some problem in their proofs. We state as follows. By the definition of a snapback repeller, if a point z is a snapback repeller of a map g, then z is an expanding fixed point of g in ${\overline{B}}_{r}(z)$ for some $r>0$, and there exists a point ${x}_{0}\in {B}_{r}(z)$ with ${x}_{0}\ne z$ such that ${g}^{m}({x}_{0})=z$ for some positive integer m. It is easy to show that there must exist a positive integer ${m}_{0}<m$ such that ${g}^{{m}_{0}}({x}_{0})\notin {B}_{r}(z)$. However, in the proofs of both [25] and [26], they all proved that the map g of the controlled system is expanding in a neighborhood ${B}_{r}(O)$ of the origin $O\in {\mathbf{R}}^{k+1}$, and there exists a point ${x}_{0}\in {B}_{r}(O)$ such that $g({x}_{0})\in {B}_{r}(O)$ and ${g}^{2}({x}_{0})=O$, and consequently O is a snapback repeller of the map g. This is a contradiction with the definition of a snapback repeller. In this paper, we use controller (3) to chaotify the linear delay system (1), which corresponds to the chaotification of linear system (4) with the following singleinput controller:
This controller is slightly different from that used in [25]. However, the problem is solved, and it is rigorously proved that system (4) with the above controller is chaotic in the sense of both Devaney and LiYorke by using the snapback repeller theory.
4 An example
In the last section, we present an example of chaotification for a linear delay difference equation with computer simulations.
Example 1 The linear delay difference equation (1) is taken as the following:
where $k\ge 1$ is a fixed integer.
Obviously, $O={(0,0,\dots ,0)}^{T}\in {\mathbf{R}}^{k+1}$ is a fixed point of equation (11). By Theorem 1, we can take controller (3) with β and ${\gamma}_{j}$, $1\le j\le k+1$, as specified in the proof, such that the output of the controlled system (5), i.e., system (2), is chaotic in the sense of both Devaney and LiYorke. In order to help better visualize the theoretical result, we take $k=1,2$ for computer simulations. For $k=1$, we can take
For $k=2$, we take
Both of them satisfy the condition (7). The simulated results show that the origin system (4), i.e., system (11), has simple dynamical behaviors, and the controlled system (5), i.e., system (2), has complex dynamical behaviors, see Figures 14.
References
 1.
Freeman WJ: Chaos in the brain: possible roles in biological intelligence. Int. J. Intell. Syst. 1995, 10: 7188. 10.1002/int.4550100107
 2.
Schiff SJ, Jerger K, Duong DH, Chang T, Spano ML, Ditto WL: Controlling chaos in the brain. Nature 1994, 370: 615620. 10.1038/370615a0
 3.
Brandt ME, Chen GR: Bifurcation control of two nonlinear of models of cardiac activity. IEEE Trans. Circuits Syst. I 1997, 44: 10311034. 10.1109/81.633897
 4.
Ditto WL, Spano ML, Nelf J, Meadows B, Langberg JJ, Bolmann A, McTeague K: Control of human atrial fibrillation. Int. J. Bifurc. Chaos 2000, 10: 593602.
 5.
Jakimoski G, Kocarev LG: Chaos and cryptography: block encryption ciphers based on chaotic maps. IEEE Trans. Circuits Syst. I 2001, 48: 163169. 10.1109/81.904880
 6.
Kocarev LG, Maggio M, Ogorzalek M, Pecora L, Yao K: Special issue on applications of chaos in modern communication systems. IEEE Trans. Circuits Syst. I 2001, 48: 13851527.
 7.
Chen GR, Lai DJ: Feedback control of Lyapunov exponents for discretetime dynamical systems. Int. J. Bifurc. Chaos 1996, 6: 13411349. 10.1142/S021812749600076X
 8.
Chen GR, Lai DJ: Anticontrol of chaos via feedback. Proc. of IEEE Conference on Decision and Control 1997, 367372.
 9.
Chen GR, Lai DJ: Feedback anticontrol of discrete chaos. Int. J. Bifurc. Chaos 1998, 8: 15851590. 10.1142/S0218127498001236
 10.
Wang XF, Chen GR: On feedback anticontrol of discrete chaos. Int. J. Bifurc. Chaos 1999, 9: 14351441. 10.1142/S0218127499000985
 11.
Shi YM, Chen GR: Chaotification of discrete dynamical systems governed by continuous maps. Int. J. Bifurc. Chaos 2005, 15: 547556. 10.1142/S0218127405012351
 12.
Shi YM, Yu P, Chen GR: Chaotification of dynamical systems in Banach spaces. Int. J. Bifurc. Chaos 2006, 16: 26152636. 10.1142/S021812740601629X
 13.
Wang XF, Chen GR: Chaotification via arbitrary small feedback controls. Int. J. Bifurc. Chaos 2000, 10: 549570.
 14.
Shi YM, Chen GR: Discrete chaos in Banach spaces. Sci. China Ser. A 2004, 34: 595609. English version: 48, 222238 (2005)
 15.
Chen GR, Shi YM: Introduction to anticontrol of discrete chaos: theory and applications. Philos. Trans. R. Soc. Lond. A 2006, 364: 24332447. 10.1098/rsta.2006.1833
 16.
Shi YM, Chen GR: Chaos of discrete dynamical systems in complete metric spaces. Chaos Solitons Fractals 2004, 22: 555571. 10.1016/j.chaos.2004.02.015
 17.
Marotto FR:Snapback repellers imply chaos in ${R}^{n}$.J. Math. Anal. Appl. 1978, 63: 199223. 10.1016/0022247X(78)901154
 18.
Li TY, Yorke JA: Period three implies chaos. Am. Math. Mon. 1975, 82: 985992. 10.2307/2318254
 19.
Devaney RL: An Introduction to Chaotic Dynamical Systems. AddisonWesley, New York; 1987.
 20.
Martelli M, Dang M, Steph T: Defining chaos. Math. Mag. 1998, 71: 112122. 10.2307/2691012
 21.
Robinson C: Dynamical Systems: Stability, Symbolic Dynamics and Chaos. CRC Press, Boca Raton; 1995.
 22.
Wiggins S: Global Bifurcations and Chaos. Springer, New York; 1988.
 23.
Banks J, Brooks J, Cairns G, Davis G, Stacey P: On Devaney’s definition of chaos. Am. Math. Mon. 1992, 99: 332334. 10.2307/2324899
 24.
Huang W, Ye XD: Devaney’s chaos or 2scattering implies LiYorke’s chaos. Topol. Appl. 2002, 117: 259272. 10.1016/S01668641(01)000256
 25.
Zhang HZ, Chen GR: Singleinput multioutput statefeedback chaotification of general discrete systems. Int. J. Bifurc. Chaos 2004, 14: 33173323. 10.1142/S0218127404011223
 26.
Kwok HS, Tang WKS: Chaotification of discretetime systems using neurons. Int. J. Bifurc. Chaos 2004, 14: 14051411. 10.1142/S0218127404009892
Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant 11101246).
Author information
Additional information
Competing interests
The author declares that he has no competing interests.
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Received
Accepted
Published
DOI
Keywords
 chaos
 chaotification
 delay difference equation
 chaos in the sense of LiYorke
 chaos in the sense of Devaney