A memristive chaotic oscillator with controllable amplitude and frequency

Zhang, Xin; Li, Chunbiao; Chen, Yudi; Iu, Herbert Ho Ching; Lei, Tengfei

doi:10.1016/j.chaos.2020.110000

Cited by 64 publications

(23 citation statements)

References 38 publications

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“…The amplitude control plays an important role in dynamical systems and has valuable applications [51][52]. But such a phenomenon has not been observed in neural networks.…”

Section: Multiple Bursting Firings With Different Amplitudesmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

101

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Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

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“…The amplitude control plays an important role in dynamical systems and has valuable applications [51][52]. But such a phenomenon has not been observed in neural networks.…”

Section: Multiple Bursting Firings With Different Amplitudesmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

101

View full text Add to dashboard Cite

show abstract

“…Engineering fields have many utilizations of chaotic attractors [2]. Some common utilizations are enlisted such as oscillations [5], [6], vibrations [7], neuron models [8], [9], control and memristor models [10]- [12], mechanical attractors [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A new 2-D multi-stable chaotic attractor and its MultiSim electronic circuit design

Vaidyanathan

Sambas²,

Mohamed

et al. 2021

IJEECS

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<span>A new multi-stable system with a double-scroll chaotic attractor is developed in this paper. Signal plots are simulated using MATLAB and multi-stability is established by showing two different coexisting double-scroll chaotic attractors for different states and same set of parameters. Using integral sliding control, synchronized chaotic attractors are achieved between drive-response chaotic attractors. A MultiSim circuit is designed for the new chaotic attractor, which is useful for practical engineering realizations.</span>

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“…In 2020, Zhang et al [17] constructed a new memristor-based Hopfield neural network (HNN) model with multi-double-scroll attractors, showing its coexisting characteristics. The same year, Zhang and his colleagues [18] applied a flux-controlled memristor to modify a variable-bootable chaotic system [19] such that it exhibits relatively robust chaotic oscillation compared with other multi-stable memristor-based systems. In the above literature, most scholars follow with interest in the natural nonlinearity of memristor while ignoring the controllability of its resistance variability.…”

Section: Introductionmentioning

confidence: 99%

Memristor-based Chaotic System with Upper-Lower Chaotic Attractors and Abundant Dynamical Behaviors

Yan

Ji’e

Wang

et al. 2021

Preprint

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Memristor, a controllable nonlinear element, is able to produce the chaotic signal easily. Most of the current researchers concentrate on the nonlinear properties of memristor, whereas its ability to control and adjust chaotic systems is often neglected. Thus a memristor-based chaotic system is designed to generate double-scroll chaotic attractors in this paper. The key features of the system are as follows: (a) when the polarity of the strength of memristor is adjusted, the upper-lower double-scroll chaotic attractors will be presented in the system. Chaotic motion of the system will be weakened and suppressed by properly selecting the strength of memristor, which enables the system to generate chaotic signals or suppress chaotic interference; (b) The system has very abundant dynamical behaviors, including sustained chaos, bistability, coexisting attractors, transient chaos, transient period and intermittency. To further explain the complex dynamics of the system, several basic dynamical behaviors, such as dissipation, symmetry, the stability of the equilibria, Poincare-maps, offset boosting control, recurrence analysis, 0-1 test analysis, and instantaneous phase analysis are displayed, either analytically or numerically. Moreover, the analogy circuit of the system is constructed. Experiment results prove that the PSPICE simulation and numerical analysis are consistent, which verifies the chaotic system's capability that produces chaos. Additionally, the proposed chaotic system has a strong immunity to any Gaussian noise with the zero mean. Therefore, a convenient method is employed to detect a weak multi-frequency signal embedded in the Gaussian noise based on the proposed chaotic system and the recursive back-stepping controller. The method based on the memristor-based chaos system provides a new train of thought for detecting weak signals, which is of great significance to promote the application of memristor.

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A memristive chaotic oscillator with controllable amplitude and frequency

Cited by 64 publications

References 38 publications

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

A new 2-D multi-stable chaotic attractor and its MultiSim electronic circuit design

Memristor-based Chaotic System with Upper-Lower Chaotic Attractors and Abundant Dynamical Behaviors

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