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This article investigates global exponential stabilization (GES) of Takagi–Sugeno (T–S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes. [ABSTRACT FROM AUTHOR]

Synchronization of a class of drive-response timescale-type nonautonomous proportional-delayed neural networks (TNPNNs) is addressed in this article. The key technique to cope with the proportional term is using comparison principle. By timescale theory, inequality technique, and comparison principle, criteria of synchronization are obtained. The method used in this article is effective to cope with TNPNNs and it is a direct approach as well by eliminating the conventional exponential transformation. The obtained results are verified with three examples. [ABSTRACT FROM AUTHOR]

This article concentrates on finite-time stabilization and energy consumption estimation for nonlinear systems with and without delay. By constructing an appropriate controller and utilizing inequality techniques, sufficient conditions are proposed to guarantee the finite-time stability of the delayed nonlinear system. Furthermore, the energy consumption produced in system controlling is estimated by inequality techniques. Then, we formulate similar results for the delay-free case. Finally, numerical examples are presented to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

This article focuses on investigating the synchronization and position/force tracking performance of the multilateral teleoperation system with asymmetric time delays. First, the synchronization and tracking error signals are proposed to transform the nonlinear dynamic system into a closed-loop system governed by the Markovian jumping parameter. Because of the presence of time delays, the information regarding the system states might become unavailable. Owing to this, a feedback control based on the hidden Markov model is developed through which the information on the current state of the actual system can be accessed through a series of observations. Moreover, the forward and backward delays of the master and slave manipulators are assumed to be asymmetric and varying with time. For the stability analysis, the Lyapunov–Krasovskii technique has been adopted for which its derivatives are dealt by employing Jensens’ inequality and extended reciprocal convex matrix inequality. Finally, simulation results were provided to validate the proposed methodology guaranteeing the closed-loop system to be asymptotically stable. [ABSTRACT FROM AUTHOR]

This article studies the consensus of hybrid second-order multiagent systems (MASs), where the hybrid MAS is constituted by the continuous-time second-order (CTSO) and discrete-time second-order (DTSO) dynamic agents. First, a hybrid consensus algorithm is proposed, where a sample-data control is considered in the CTSO subsystems. Under the proposed hybrid consensus algorithm, the convergence of the hybrid second-order MAS matrix is analyzed. Second, a sufficient and necessary condition is proposed, which indicates that the controller parameters, such as coupling gains and the sampling interval, and eigenvalues of network topology have a significant impact on the system consensus. Finally, several simulation examples are presented to prove the validity of the results. [ABSTRACT FROM AUTHOR]

This article deals with the asymptotic stability and synchronization of fractional-order neural networks (FONNs) with unbounded time-varying delays, in which FONNs can be the general nonlinear system. First, the existence of the equilibrium point of the nonlinear system is obtained, and the μ-type stability of fractional-order nonlinear systems is proved by the analytical method. Second, several sufficient conditions are derived to guarantee the μ-type stability of FONNs by taking easily implemented parameters. Moreover, the μ-type synchronization of drive-response systems is established by designing the linear controller. Especially, μ-type stability is the general stability, including Mittag-Leffler stability and power-stability, which is the extension of the Mittag-Leffler stability of FONNs. Finally, two numerical examples are presented to show the effectiveness of the results. [ABSTRACT FROM AUTHOR]

This paper proposes a unified framework to design sliding-mode control for stabilization of delayed memristive neural networks (DMNNs) with external disturbances. Under the presented framework, finite-time stabilization, and fixed-time stabilization of the controlled DMNNs can be, respectively, obtained by choosing different values for a specific control parameter. It is proved that the system responses can be made reaching the designed sliding-mode surface in finite and fixed time, and then stay on it. Moreover, it also illustrates that the inevitable external disturbances can be rejected by the designed sliding-mode control. Finally, the efficiency and superiority of the obtained main results are verified by comparisons with related works and numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates Cohen–Grossberg neural networks (CGNNs) with unbounded time-varying delays. The existence condition of multiple equilibrium points is established by the algebraic inequality method. And then, the ${\psi }$ -type stability is studied by analysis method for CGNNs with unbounded time-varying delays, and the relative convergence rate can be adjusted by the selection of ${\psi }$ -type functions. Moreover, by introducing the topological degree, the algebraic sum of the number of equilibria remains unchanged for any smooth function in the specified metric space. Finally, one numerical example is implemented to show the validity of the presented results. [ABSTRACT FROM AUTHOR]

This paper is concerned with multiple Lagrange stability under perturbation for recurrent neural networks with time-varying delays. This is different from traditional Lagrange stability, which means that multiple Lagrange stability under perturbation holds for any disturbance of initial value and any structural perturbation, within a specified and well-characterized set. In this paper, multiple Lagrange stability under perturbation with respect to a finite number of trivial solutions is established, which has good robustness. Under certain perturbation, the core of the proof of the boundedness of trajectories in mutually disjoint subregions is to employ the generalized differential inequality. The results supplement and extend some previous results, and they are applicable to robust analysis of multiple equilibria. Finally, numerical calculation and simulations are implemented to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the $\phi $ -type stability and robust stability for a general class of stochastic reaction–diffusion neural networks (SRDNNs) with Dirichlet boundary conditions, infinite discrete time-varying delays, and infinite continuously distributed delays. By virtue of inequality techniques, properties of $\boldsymbol M$ -matrix, and theories of stochastic analysis, several sufficient criteria are obtained to guarantee the almost sure $\boldsymbol \phi $ -type stability, $\boldsymbol p$ th moment $\boldsymbol \phi $ -type stability, and $\boldsymbol \phi $ -type robust stability of the underlying SRDNNs with hybrid unbounded time delays. With appropriate choices of the function $\boldsymbol \phi $ , the $\boldsymbol \phi $ -type stability reduces to the exponential stability, polynomial stability, and logarithmic stability. Additionally, the developed results herein include some existing ones as special cases. A numerical simulation is performed to substantiate the effectiveness and superiority of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper investigates some dynamic behaviors for a fractional-order complex-valued memristive neural networks (FCVMNNs) with delays. A new mathematical expression of the complex-value memductance (memristance) is proposed according to the feature of the complex-valued memristor-based neural networks and a new class of FCVMNNs with delays is designed. Based on the framework of Filippov solution and differential inclusion theory, the sufficient conditions are given first to guarantee the global uniform asymptotic stability for the new FCVMNNs with delays, by using fractional-order Leibniz rule and a Razumikhin-type method. In addition, a complex-value adaptive controller is designed to achieve ultimate Mittag–Leffler synchronization between two FCVMNNs with delays. Numerical simulations are given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

The existing results of passivity for neural networks mainly concentrated on first-order derivative of the states, whereas it is also significant to study the passivity with high-order derivative. In this paper, a class of memristor-based inertial neural networks (MINNs) with external inputs and outputs are concerned. First, by choosing appropriate variable transformation, the original networks are rewritten as first-order differential equations. Then a criterion of passivity for the MINNs is presented by nonsmooth analysis and linear matrix inequality (LMI) techniques, which could also result in NP-hard problem since it needs at least exponential-time to solve the passivity condition. Meanwhile, based on the obtained passivity criterion, asymptotic stability criterion is accordingly derived for MINNs. In order to avoid the NP-hard problem, we employ matrix-analysis-techniques with the property that the difference-matrix between the given matrix and the proposed matrix is seminegative definite. Then the time complexity for solving the proposed passivity criterion is reduced to constant-time with respect to the number of LMIs. Robust passivity for MINNs is also studied for bounded uncertain parameters. The MINN widens the application ranges for designing neural networks. Finally, relevant simulation examples are given to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper presents new theoretical results on the multistability analysis of a class of recurrent neural networks with nonmonotonic activation functions and mixed time delays. Several sufficient conditions are derived for ascertaining the existence of 3^n equilibrium points and the exponential stability of 2^n equilibrium points via state space partition by using the geometrical properties of activation functions and algebraic properties of nonsingular M -matrix. Compared with existing results, the conditions herein are much more computable with one order less linear matrix inequalities. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of the 2^n equilibrium points can be larger than their originally partitioned subspaces. Three numerical examples are elaborated with typical nonmonotonic activation functions to substantiate the efficacy and characteristics of the theoretical results. [ABSTRACT FROM PUBLISHER]