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This paper deals with global synchronization problem of multiple discrete-time Markovian jump memristor-based neural networks (DTMJMNNs) with mixed mode-dependent delays via a novel event-triggered impulsive coupling control (ETICC). The parameters of the multiple DTMJMNNs and the mixed time delays (both discrete and distributed delays) switch randomly according to a Markov chain. In the ETICC strategy, the controller does not work all the time, but only works at impulse instants determined by specific events. In particular, the coupling matrix can be non-Laplacian. By using the Lyapunov stability theory, linear matrix inequalities (LMIs), and the Kronecker product, some sufficient conditions for global synchronization of multiple DTMJMNNs under the event-triggered strategy are derived. Two examples are presented to test the validity of the theoretical analysis results. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the convergence properties of the max-consensus protocol in presence of asynchronous updates and bounded time delays on directed static networks. The work is motivated by real-world applications in distributed decision-making systems, for which max-consensus is an effective paradigm. The main result of this paper is that the strongly connectedness of the directed communication network is a sufficient condition for the asynchronous max-consensus protocol to let a distributed system converge in finite time. Implementation issues are also taken into account, by complementing the theoretical analysis with the definition of a mechanism to detect convergence in a distributed fashion. Finally, a numerical example is given, highlighting both the issues related to the failure of synchronous protocols applied to asynchronous settings and the effectiveness of the proposed asynchronous framework. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamical multisynchronization and static multisynchronization problem for delayed coupled multistable neural networks with fixed and switching topologies. To begin with, a class of activation functions as well as several sufficient conditions are introduced to ensure that every subnetwork has multiple equilibrium states. By constructing an appropriate Lyapunov function and by employing impulsive control theory and the average impulsive interval method, several sufficient conditions for multisynchronization in terms of linear matrix inequalities (LMIs) are obtained. Moreover, a unified impulsive controller is designed by means of the established LMIs. Finally, a numerical example is presented to demonstrate the effectiveness of the presented impulsive control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption. [ABSTRACT FROM PUBLISHER]

This paper addresses the telecoordinated control of multiple robots in the simultaneous presence of asymmetric time-varying delays, nonpassive external forces, and uncertain kinematics/dynamics. To achieve the control objective, a neuroadaptive controller with utilizing prescribed performance control and switching control technique is developed, where the basic idea is to employ the concept of motion synchronization in each pair of master–slave robots and among all slave robots. By using the multiple Lyapunov–Krasovskii functionals method, the state-independent input-to-output practical stability of the closed-loop system is established. Compared with the previous approaches, the new design is straightforward and easier to implement and is applicable to a wider area. Simulation results on three pairs of three degrees-of-freedom robots confirm the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

In this paper, the synchronization problem is investigated for a class of nonlinear delayed dynamical networks with heterogeneous impulsive effects. The intrinsic properties of heterogeneous impulses are that impulsive strengths are inhomogeneous in both time and space domains, i.e., the impulsive effect in each node is not only nonidentical from each other, but also time-varying at different impulsive instants. The purpose of the addressed problem is to derive synchronization criteria such that, the nonlinear delayed dynamical networks with heterogeneous impulses can be synchronized to a desired state. By means of a time-dependent Lyapunov function and the comparison principle, several sufficient conditions are established under which nonlinear dynamical networks with heterogeneous impulsive effects are exponentially synchronized to a desired state. An example is given to show the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problems of exponential stability analysis and synchronization of discrete-time switched delayed neural networks. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time-delays. Benefitting from the delay partitioning method and the free-weighting matrix technique, the conservatism of the obtained results is reduced. In addition, the decay estimates are explicitly given and the synchronization problem is solved. The results reported in this paper not only depend upon the delay, but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the adaptive exponential synchronization in both the mean square and the almost sure senses for an array of N identical Markovian stochastic coupled neural networks of neutral-type with time-varying delay and random coupling strength. The generalized Lyapunov theorem of the exponential stability in the mean square for the neutral stochastic Markov system with the time-varying delay is first established. The time-varying delay in the system is assumed to be a bounded measurable function. Then, sufficient conditions to guarantee the exponential synchronization in the mean square for the underlying system are developed under an adaptive feedback controller, which are given in terms of the \mathcal M -matrix and the algebraic inequalities. Under the same conditions, the almost sure exponential synchronization is also presented. A numerical example is given to show the effectiveness and potential of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents an asynchronous digital technique for the realization of an integrator that takes as input the time-difference between two rising edges of digital signals and produces a corresponding time-difference output signal. The key element of this circuit is a time-memory cell called TLatch. This circuit has the ability to store the time-difference between two edges and allow its retrieval at a later time. By using two TLatches in parallel, a dual-path high-throughput integrator is proposed. Internal mismatches in delays can be removed using a simple calibration algorithm that aligns the frequency of two internal oscillators, thereby eliminating the need for trimming or any reference element. The proposed architecture is fabricated in 1.2 V 0.13-μm IBM CMOS technology and the experimental results confirm the integration operation. [ABSTRACT FROM PUBLISHER]

This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the $\omega $ -measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results. [ABSTRACT FROM PUBLISHER]

This paper addresses the problems of synchronization and state estimation for a class of discrete-time hierarchical hybrid neural networks (NNs) with time-varying delays. The hierarchical hybrid feature consists of a higher level nondeterministic switching and a lower level stochastic switching. The latter is used to describe the NNs subject to Markovian modes transitions, whereas the former is of the average dwell-time switching regularity to model the supervisory orchestrating mechanism among these Markov jump NNs. The considered time delays are not only time-varying but also dependent on the mode of NNs on the lower layer in the hierarchical structure. Despite quantization and random data missing, the synchronized controllers and state estimators are designed such that the resulting error system is exponentially stable with an expected decay rate and has a prescribed H\infty disturbance attenuation level. Two numerical examples are provided to show the validity and potential of the developed results. [ABSTRACT FROM AUTHOR]

Multiagent systems (MASs) are ubiquitous in our real world. There is an increasing attention focusing on the consensus (or synchronization) problem of MASs over the past decade. Although there are numerous results reported on the convergence of a discrete-time MAS based on the infinite products of matrices, few results are on the convergence rate. Because of the switching topology, the traditional eigenvalue analysis and the Lyapunov function methods are both invalid for the convergence rate analysis of an MAS with a switching topology. Therefore, the estimation of the convergence rate for a discrete-time MAS with time-varying delays remains a difficult problem. To overcome the essential difficulty of switching topology, this paper aims at developing a contractive-set approach to analyze the convergence rate of a discrete-time MAS in the presence of time-varying delays and generalized coupling coefficients. Using the proposed approach, we obtain an upper bound of the convergence rate under the condition of joint connectivity. In particular, the proposed method neither requires the nonnegative property of the coupling coefficients nor the basic assumption of a uniform lower bound for all positive coupling coefficients, which have been widely applied in the existing works on this topic. As an application of the main results, we will show that the classical Vicsek model with time delays can realize synchronization if the initial topology is connected. [ABSTRACT FROM PUBLISHER]