Dynamics analysis of Wien-bridge hyperchaotic memristive circuit system

Ye, Xiaolin; Mou, Jun; Luo, Cunjin; Wang, Zhisen

doi:10.1007/s11071-018-4100-x

Cited by 71 publications

(34 citation statements)

References 31 publications

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“…As shown in Figure 6, the region of a ∈ [− 20,10] and b ∈ [−20,10] can be divided into two parts. The blue part satisfies the above equation, so it represents a stable region.…”

Section: Dissipativity and Stabilitymentioning

confidence: 99%

“…Reference [19] presented and analyzed a new chaotic circuit, which was composed of a meminductor emulator and an active memristor emulator. Reference [20] constructed a memristor-based hyperchaotic Wien-bridge oscillator and analyzed its dynamical behaviors. In Reference [21], an inductor-free chaotic circuit containing two memristors was proposed.…”

Section: Introductionmentioning

confidence: 99%

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Coexisting Attractors and Multistability in a Simple Memristive Wien-Bridge Chaotic Circuit

Song

Yuan

2019

Entropy

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In this paper, a new voltage-controlled memristor is presented. The mathematical expression of this memristor has an absolute value term, so it is called an absolute voltage-controlled memristor. The proposed memristor is locally active, which is proved by its DC V–I (Voltage–Current) plot. A simple three-order Wien-bridge chaotic circuit without inductor is constructed on the basis of the presented memristor. The dynamical behaviors of the simple chaotic system are analyzed in this paper. The main properties of this system are coexisting attractors and multistability. Furthermore, an analog circuit of this chaotic system is realized by the Multisim software. The multistability of the proposed system can enlarge the key space in encryption, which makes the encryption effect better. Therefore, the proposed chaotic system can be used as a pseudo-random sequence generator to provide key sequences for digital encryption systems. Thus, the chaotic system is discretized and implemented by Digital Signal Processing (DSP) technology. The National Institute of Standards and Technology (NIST) test and Approximate Entropy analysis of the proposed chaotic system are conducted in this paper.

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“…As shown in Figure 6, the region of a ∈ [− 20,10] and b ∈ [−20,10] can be divided into two parts. The blue part satisfies the above equation, so it represents a stable region.…”

Section: Dissipativity and Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coexisting Attractors and Multistability in a Simple Memristive Wien-Bridge Chaotic Circuit

Song

Yuan

2019

Entropy

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“…Model of the Memristors. According to the mathematical model of flux-controlled memristor which was put forward in [19,20], we can obtain that memristor can be defined as a two-terminal element. For a smooth passive flux-controlled memristor, the magnetic flux ξ between the terminals is a function of the electric charge q that passes through the device, and its state variable are…”

Section: Mixed Chaotic Circuit Based On Memristorsmentioning

confidence: 99%

Complexity Analysis of a Mixed Memristive Chaotic Circuit

Mou

Luo

et al. 2018

Complexity

Self Cite

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In this paper, we design a chaotic circuit with memristors, which consists of two flux-controlled memristors and a charge-controlled memristor, and the dimensionless mathematical model of the circuit was established. Using the conventional dynamic analysis methods, the equilibrium point set and stability of the chaotic system were analyzed, and the distribution of stable and unstable regions corresponding to the memristor initial states was determined. Then, we analyze the dynamical behaviors with the initial states of the memristors and the circuit parameter of the circuit system, respectively. By using spectral entropy (SE) and C0 complexity algorithms, the dynamic characteristics of the system were analyzed. In particular, the 2D and 3D complexity characteristics with multiple varying parameters were analyzed. Some peculiar physical phenomenon such as coexisting attractors was observed. Theoretical analysis and simulation results show that the chaotic circuit has rich dynamical behaviors. The complicated physical phenomenon in the new chaotic circuit enriches the related content of chaotic circuit with memristors.

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“…Neural networks are a type of mathematical model that simulates the thinking patterns of the human brain, which is used to imitate the structure and thinking mode of neural network in the human brain [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

Fang

Yin

et al. 2020

Mathematical Problems in Engineering

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This paper investigates the impulsive synchronization of time delay coupled neural networks. Based on the Lyapunov stability theory and impulsive control method, a distributed delayed impulsive controller is designed to realize synchronization of the coupled neural networks. A new impulsive delayed inequality is proposed, where the control effect of distributed delayed impulses is fully considered. In addition, a suitable Lyapunov-like function is established to prove the stability of the synchronization system. Numerical simulation examples are introduced to illustrate the effectiveness and feasibility of the main results.

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Dynamics analysis of Wien-bridge hyperchaotic memristive circuit system

Cited by 71 publications

References 31 publications

Coexisting Attractors and Multistability in a Simple Memristive Wien-Bridge Chaotic Circuit

Coexisting Attractors and Multistability in a Simple Memristive Wien-Bridge Chaotic Circuit

Complexity Analysis of a Mixed Memristive Chaotic Circuit

Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

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