Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this paper.Both continuous and discontinuous activations are considered forMNNs.And the mixed delays which are closer to reality are taken into the system.Besides,two kinds of control schemes are proposed,including feedback and adaptive control strategies.Based on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are acquired.Moreover,the upper bound of settling time(ST)which is independent of the initial values is given.Finally,the feasibility of our theory is attested by simulation examples.

Synchronization of Fractional-Order Memristor-Based Chaotic System via Adaptive Control

The synchronization of a novel fractional-order memristorbased chaotic system is investigated.And an adjustable adaptive controller is designed to achieve the synchronization of this system.By adjusting the control coefficients of the controller,drive-response system can achieve many different types of synchronization such as adaptive synchronization,projective synchronization and antisynchronization.The sufficient condition for the synchronization has been analyzed by the stability theory of fractional-order differential system.Finally,numerical simulations are used to demonstrate that the proposed adaptive controller is effective and correct.

Memristor initial-boosted coexisting plane bifurcations and its extreme multi-stability reconstitution in two-memristor-based dynamical system

Initial-dependent extreme multi-stability and offset-boosted coexisting attractors have been significantly concerned recently.This paper constructs a novel five-dimensional(5-D)two-memristor-based dynamical system by introducing two memristors with cosine memductance into a three-dimensional(3-D)linear autonomous dissipative system.Through theoretical analyses and numerical plots,the memristor initial-boosted coexisting plane bifurcations are found and the memristor initial-dependent extreme multi-stability is revealed in such a two-memristor-based dynamical system with plane equilibrium.Furthermore,a dimensionality reduction model with the determined equilibrium is established via an integral transformation method,upon which the memristor initial-dependent extreme multi-stability is reconstituted theoretically and expounded numerically.Finally,physically circuit-implemented PSIM(power simulation)simulations are carried out to validate the plane offset-boosted coexisting behaviors.

Periodic oscillation of memristor-based recurrent neural networks with time-varying delays and leakage delays

Purpose–The purpose of this paper is to investigate the existence and global exponential stability of periodic solution of memristor-based recurrent neural networks with time-varying delays and leakage delays.Design/methodology/approach–The differential inequality theory and some novel mathematical analysis techniques are applied.Findings–A set of sufficient conditions which guarantee the existence and global exponential stability of periodic solution of involved model is derived.Practical implications–It plays an important role in designing the neural networks.Originality/value–The obtained results of this paper are new and complement some previous studies.The innovation of this paper concludes two aspects:the analysis on the existence and global exponential stability of periodic solution of memristor-based recurrent neural networks with time-varying delays and leakage delays is first proposed;and it is first time to establish the sufficient criterion which ensures the existence and global exponential stability of periodic solution of memristor-based recurrent neural networks with time-varying delays and leakage delays.

Memristor hyperchaos in a generalized Kolmogorov-type system with extreme multistability

Memristor chaotic systems have aroused great attention in recent years with their potentials expected in engineering applications.In this paper,a five-dimension(5D)double-memristor hyperchaotic system(DMHS)is modeled by introducing two active magnetron memristor models into the Kolmogorov-type formula.The boundness condition of the proposed hyperchaotic system is proved.Coexisting bifurcation diagram and numerical verification explain the bistability.The rich dynamics of the system are demonstrated by the dynamic evolution map and the basin.The simulation results reveal the existence of transient hyperchaos and hidden extreme multistability in the presented DMHS.The NIST tests show that the generated signal sequence is highly random,which is feasible for encryption purposes.Furthermore,the system is implemented based on a FPGA experimental platform,which benefits the further applications of the proposed hyperchaos.

A generalized model of TiOx-based memristive devices and its application for image processing

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.