A Novel Memristor Chaotic System with a Hidden Attractor and Multistability and Its Implementation in a Circuit

By introducing an ideal and active flux-controlled memristor and tangent function into an existing chaotic system, an interesting memristor-based self-replication chaotic system is proposed. +e most striking feature is that this system has infinite line equilibria and exhibits the extreme multistability phenomenon of coexisting infinitely many attractors. In this paper, bifurcation diagrams and Lyapunov exponential spectrum are used to analyze in detail the influence of various parameter changes on the dynamic behavior of the system; it shows that the newly proposed chaotic system has the phenomenon of alternating chaos and limit cycle. Especially, transition behavior of the transient period with steady chaos can be also found for some initial conditions. Moreover, a hardware circuit is designed by PSpice and fabricated, and its experimental results effectively verify the truth of extreme multistability.


Introduction
In 1963, the first chaotic system was discovered by Lorenz. Since then, many scientists have constructed many new chaotic systems, such as Chen system, Lu system, and Jerk system [1,2]. en, in 1971, Chua proposed the memristor, the fourth element after resistor [3,4], capacitor, and inductor. People have studied chaotic systems based on memristor design. Compared with other classical chaotic systems [5], memristive nonlinear systems have more complex chaotic characteristics [6][7][8][9][10]. Some articles reported the phenomenon of memristive multistable state.
In addition to the sensitivity of the system to the parameters, it also depends on the initial value of the memristor. Due to the introduction of the ideal memristor [11][12][13][14][15][16], the dynamic system based on the memristor produces infinite equilibria, such as line equilibria and surface equilibria. ese equilibria are related to the initial state variables of the memristor. ese memristor models not only exhibit complex chaotic behaviors but also produce multistability phenomena [17,18]. As we all know, multistability is the coexistence behavior of two or more attractors under the same parameters and different initial conditions [19][20][21]. Bao et al. [22] introduced an ideal and active flux-controlled memristor into an existing hypogenetic chaotic jerk system, an interesting memristor-based chaotic system with a hypogenetic jerk equation, and proposed circuit forms. e most striking feature is that this system has four line equilibria and exhibits the extreme multistability phenomenon of coexisting infinitely many attractors. Jafari et al [23] propose a newly parameter estimation method on both an ordinary chaotic system and a chaotic system with extreme multistability. It proves the importance of that difference better by comparing the efficiency of the chaotic system. Recently, the introduction of trigonometric functions, such as the tangent function, sine function, hyperbolic tangent function [24,25], and hyperbolic sine function, has made the system produce multiscroll attractors, multiwing attractors, infinite coexistence attractors, and attractor duplication phenomena [26,27]. is nonlinear performance is caused by the periodicity of trigonometric functions [28,29]. is special feature has been reported in some chaotic article recently. However, there are relatively few studies on homogeneous multistability in the memristor chaotic systems of trigonometric functions [30][31][32][33][34].
Inspired by the abovementioned ideas, an interesting memristor-based chaotic system is constructed in this paper, which is achieved by introducing a tangent function (tan(z)) and an ideal and active flux-controlled memristor with absolute value nonlinearity into an existing chaotic system boostable VB18 [31]. e newly proposed memristive system has infinite line equilibria and can exhibit the initialcondition-dependent extreme multistablity phenomenon of coexisting infinitely many attractors, which has seldom been reported in the academic literature. e rest of this paper is organized as follows. In Section 2, a novel memristive system with infinite line equilibria is presented, upon which the stability for the infinite line equilibria are explored. In Section 3, parameter-dependent change is investigated by bifurcation diagrams and Lyapunov exponent spectra and coexisting intermittent chaos behavior revealed by phase portraits. In Section 4, initial-conditiondependent extreme multistability is investigated by coexisting bifurcation diagrams and Lyapunov exponent spectra, and coexisting infinitely many attractors' behavior and symmetric behavior are revealed by phase portraits. In Section 5, an implementation circuit is designed and PSpice circuit simulations are performed to verify the initial-condition-dependent dynamical behaviors of coexisting infinitely many attractors. In Section 6, the conclusions are summarized.

Model Description
According to the chaotic system VB18 reported in [31], a new memristive system can be easily constructed by the tangent function to substitute a z status variable and by introducing utilizing memristor in the chaotic system with an existing nonlinear dynamical system boostable VB18. A VB18 chaotic system is described as e memductance function of the desired flux-controlled memristor is expressed as where m and n are two memristor parameters with positive values. e abovementioned model (2) is used to describe an ideal and active flux-controlled memristor with an absolute value nonlinearity. rough introducing the newly proposed memristor featured by (2) and trigonometric function (tan(z)) into the chaotic system in [1], a new kind of memristor chaotic system is established, which can be mathematically modeled as where x, y, z, and u are the state variables of system (3), a, b, and c are the control parameters of system (3), and W(u) � − m + n|u| is the normalized memductance function. u in formula (3) is φ in formula (2). e voltage-current curves of the memristor with different values of the frequency f are plotted in Figure 1. It can be seen that the pinched hysteresis loop gradually shrinks with f increasing from 1.07 to 2.07, then to 3.07 in Figure 1(a), and keeps pinched at the original point with the origin, which is a typical 8-like hysteresis loop. It can be seen that W(u) stands for the memductance related to the magnetic flux u in Figure 1(b) implying that system (2) satisfies the definition of the memristor.

Stability Analysis of the Equilibrium Point.
When a � 5.8, b � 7.9, m � 0.02, and n � 0.06, x, y, z, and w are state variables of system (3). e equilibrium points of system (3) can be easily calculated by setting the left-hand side to zero, which are not associated with the memductance function.
e Jacobian matrix of system (3) at the equilibrium point E � (1, 1, 0, u) is yielded as where a 1 � a 0 � 1, a 2 � 0.348|h| − 0.116, and a 3 � 0.948 |h| − 0.316, and the abovementioned characteristic 2 Mathematical Problems in Engineering   polynomial implies that Jacobian matrix (5) has three nonzero roots and one zero root. For these roots, Routh-Hurwitz conditions are given as where k � 1, 2, 3, and Obviously, the eigenvalues of (6) are relevant to the algebraic symbols u. (3) has one positive real root and two complex conjugate roots with negative real parts. e results imply that the infinite line equilibiria of system (3) only consist of unstable saddle points and unstable saddle-foci.

Bifurcation Analysis.
e dynamic properties of the system will be changed by the changing parameters, which will make the system show the phenomena of chaotic periodic divergence.
For system (3), the parameters a and b effectively determine the dynamic behavior, as shown in Figure 3. When the initial value [1, 1, 0, 5] is fixed, the parameter b � 7.9. When the parameter a varies in [2,11], chaos and periodic oscillation appear alternately as shown in Figure 3. e largest finite-time Lyapunov exponents are calculated and plotted with varying parameter a, as shown in Figure 3. As we can be seen from Figure 3, when a is in the interval [6.23, 6.355] and [4.256, 4.455], the largest finite-time Lyapunov exponents are equal to 0, and the system produces cycle-4 attractors. When a is in the interval [4.85, 4.95] and [6.25, 7.25], the system produces cycle-2 attractors. When a is in the interval [7.35, 11], the system produces cycle-1 attractors. e change of parameter a makes system (3) show inverted period bifurcation behavior. When a > 7.2, the antiperiod bifurcation of the system has chaos and reaches a steady state. en, the same initial value [1, 1, 0, 5] is fixed, when the parameter a � 5.8, and the parameter b changes in [4,13]; the Lyapunov exponent spectrum and bifurcation diagram of system  [11.533, 12.155] and [12.566, 13.215], the system is in a chaotic state. Obviously, when parameter b is set to different values, the system shows periodic, chaotic, single scroll, double scroll, and other dynamic behaviors as shown in Figure 5.

Multistable State Analysis
When the same parameter takes different initial values, two or more attractors are called coexistent attractors or multiple attractors, which is called the multistable state. e fourdimensional initial value of system (3) has different attractors when it takes different values. It can generate various types of coexisting attractors.

Dynamics with Respect to Memristor Initial Condition
(u 0 ). In most memristor systems, the value of a memristor has some relations with the initial condition, so we have different oscillating dynamics depending on whether the initial data of the internal variable causes different oscillations.
is phenomenon is widely studied and is known as extreme polystability. In system (3), the control parameters are set as a � 5.8, b � 7.9, m � 0.02, and n � 0.06, the initial conditions are assigned x (0) � 0, y (0) � 1, and z (0) � 0, and the memristor initial condition u (0) is taken as the bifurcation parameter. When the memristor initial condition u 0 varies in (− 30, 30), system (3) has a different stable state, in which the Lyapunov exponent and bifurcation evolution are shown in Figures 6(a) and 6(b) and 7(a) and 7(b). When the memristor initial condition u (0) is gradually changed, system (3) shows multiple period-doubling bifurcations and the chaotic state intermittent existence phenomenon. Consequently, the results of Figures 6 and 7 demonstrate that, under different memristor initial conditions, there are completely different dynamical behaviors in system (3), leading to the coexisting phenomenon of many attractors. As can be seen from Figure 6(a), with the change of initial value u 0 , system (3) shows extremely complex dynamic behavior, including single period, double period, chaotic behavior, and period doubling. In particular, it can be found that the dynamic behavior of system (3) is extremely sensitive to the disturbance from small changes in initial conditions, and the initial value u 0 has a completely different dynamic behavior in the region around parameter 0. It is remarked that when the initial conditions of x (0), y (0), and z (0) are assigned to different values as shown in Figure 7, system (3) has different bifurcation behaviors as the memristor initial condition u (0) is varied, which further indicates that there exists coexisting infinitely many attractors' behavior in system (3).
To better represent extreme multisteadiness, the typical phase portraits of attractors under different u 0 are shown in Figure 8. When the initial value u 0 is different, the state of system (3) is shown in Table 1.

Dynamics with Respect to Memristor Initial Condition (Y 0 ).
e control parameters of system (3) are kept unchanged, the initial conditions are assigned as (1 Y 0 0 5), and the initial condition Y 0 is taken as the bifurcation parameter. When Y 0 is varied in the region [− 10, 10], the bifurcation diagram of the state variable Y 0 and its Lyapunov exponent spectra are plotted in Figures 9(a) and  9(b). When the initial condition Y 0 is increased from 0, system (3) goes from the normal chaotic state into the quasiperiodic state, breaks into the weak chaotic state, and then, turns into the normal chaotic state.
Near Y 0 � 1.85, system (3) degrades into the periodic state via chaos, but near Y 0 � 2.09, system (3) abruptly changes into the chaotic state with periodic stats bifurcation. When Y 0 is further increased, system (3) jumps into the periodic state again at Y 0 � 2.5. In the parameter region [1.85, 2.09] of Y 0 , system (3) mainly operates in the periodic state. In the parameter region [2.13, 8.45] of Y 0 , system (3) mainly operates in the exhibits weak chaotic behaviors but exhibits weak chaotic behaviors near Y 0 � 2.24, 3.22. erefore, system (3) displays periodic state alternate with chaos. An example is taken to analyze the influence of the change of the initial value of Y 0 on the system. It can be seen that the initial value Y 0 is positive and negative, and system (3) has symmetry. When fixing a � 5.8 and b � 7.9 and initial value Y 0 changes in the interval [− 10, 10], the phase diagram of the system is symmetrically distributed in the x-y plane, as shown in Figure 10. When the value of Y 0 is 1.85, the system is in the cycle-3 stats, when the value of Y 0 is 2.215, the system is in the cycle-4 stats, and when the value of Y 0 is 3.214, the system is in the cycle-6 stats. When the value of Y 0 is 1, the system is in a chaotic state.
us, the results of Figure 10 imply that there is coexisting many attractors' behavior in system (3).

Dynamics with Respect to Memristor Initial Condition
(z 0 ). Furthermore, for the periodic tangent function, all the infinite countless attractors can be self-reproduced in the      Mathematical Problems in Engineering     Figure 11. us, the results of Figure 11 imply that there is coexisting infinitely many attractors' behavior in system (3). e bifurcation diagram and Lyapunov exponent spectrum under the change of the initial value z 0 are shown in Figure 12; it can be seen that when the initial value z 0 takes different values, system (3) changes from single-period, double-period, three-period and four-period, quasiperiodic, and chaotic attractors; here, four typical attractors including   Figure 13. When the value of z 0 is − 0.55, the system is in the cycle-1 state, when the value of z 0 is − 0.49, the  (2)      system is in the cycle-2 state, when the value of z 0 is 0.12, the system is in the cycle-3 state, when the value of z 0 is − 0.32, the system is in the cycle-4 state, and when the value of z 0 is 0.24, the system is in the quasiperiodic attractor state. When the value of z 0 is 0.8, the system is in a chaotic state. us, the results of Figure 12 imply that there is coexisting many attractors' behavior in system (3).

Circuit Implementation
e analog circuit of system (3) is designed as shown in Figure 14 with the following circuit equation: e circuit consists of four channels to realize the integration, addition, subtraction, and nonlinear operations including absolute value function and quadratic nonlinearity, as shown in Figure 14. e main circuit with the circuit form is plotted in Figure 14(a), where x, y, and z represent three state variables of capacitor voltages, respectively, and RC stands for the time constant of the integrators. e equivalent realization unit circuit of the flux-controlled memristor W(u) is depicted in Figure 14(b), where u is the inner state variable of capacitor voltage in the memristor. e operational amplifier 741 performs the addition and integration, and the analog multiplier AD633 performs the nonlinear product operation. e circuit is powered by ±15 V. When the system parameters a � 5.8.2 and b � 7.9, the corresponding circuit element parameters can be selected as e circuit simulation is shown in Figure 14. e corresponding memristor circuit element parameters can be selected as R40 � R36 � 470 Ω, R37 � 50 KΩ, R39 � 16.6 KΩ, and R38 � 1 KΩ. e circuit simulation diagram and a plot of pinched hysteresis loop of the memristor are shown in Figure 15. e initial voltage of the capacitor is selected as V 1 � V 2 � 1 V and V 2 � − 1 V, V 3 � 0 V, and V 4 � 5 V, and the circuit simulation is shown in Figure 16(a). It can be seen that the attractors in the x-y phase diagram are symmetrically distributed. e initial voltage of the capacitor is selected as V 1 � V 2 � 1 V, V 3 � π V and V 3 � − π V, and V 4 � 5 V, and the circuit simulation is shown in Figure 16(b). It can be seen that the y-z phase diagram shows the growth distribution of attractors. Corresponding to various preset initial conditions given in Figure 8, the PSpice-simulated results are yielded and shown in Figures 17(a) and 17(b). e results in Figures 16 and 17 just verify the complex phenomenon revealed in the Matlab numerical simulations. Additionally, in consideration of different time scales between the mathematical model (3) and the circuit model (9), there exist some differences between the results in Figure 17 and those in Figure 8, but both show the initial-condition-dependent dynamical behavior of period with steady chaos.

Conclusions
A new memristive self-replicating attractor system was obtained by introducing a tangent function and memristor into the classical system. In this case, an infinite line of equilibrium is produced, causing the phenomenon of alternating chaos and period. We analyzed the change of the initial value of each variable in the four-dimensional system, a dynamic behavior in which the transient period and steady state chaos alternately appear depending on the initial value change is newly discovered, and the super multistable state was discussed. e phenomenon of attractor self-replication appears due to the introduction of the tangent function. e results of the new system imply that there is coexisting infinitely many attractors' behavior. e initial-conditiondependent dynamical behaviors of coexisting infinitely many attractors and transient period are finally validated by hardware experiments and PSpice circuit simulations, which could enhance security for possible secure communication.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper. Mathematical Problems in Engineering 15