Global dissipativity of delayed neural networks with impulses

In this paper, we investigate the problem of global exponential dissipativity of neural networks with variable delays and impulses. The impulses are classified into three classes: input disturbances, stabilizing and “neutral” type—the impulses are neither helpful for stabilizing nor destabilizing the neural networks. We handle the three types of impulses in a uniform way by using the excellent ideology introduced recently. To this end, we propose new techniques which coupled with more general Lyapunov functions to realize the ideology and it is shown that they are more effective. Exponential dissipativity conditions are established in terms of linear matrix inequalities (LMIs) and these conditions can be straightforwardly reduced to exponential stability conditions. Numerical results are given to show that the obtained conditions are effective and less conservative than the existing ones.     

Adaptive synchronization for delayed neural networks with stochastic perturbation

In this paper, an adaptive feedback controller is designed to achieve complete synchronization of unidirectionally coupled delayed neural networks with stochastic perturbation. LaSalle-type invariance principle for stochastic differential delay equations is employed to investigate the globally almost surely asymptotical stability of the error dynamical system. An example and numerical simulation are given to demonstrate the effectiveness of the theory results.     

Asymptotic stability of delayed fractional-order fuzzy neural networks with impulse effects

In this paper, we investigate the asymptotic stability of fractional-order fuzzy neural networks with fixed-time impulse and time delay. According to the fractional Barbalat’s lemma, Riemann–Liouville operator and Lyapunov stability theorem, some sufficient conditions are obtained to ensure the asymptotic stability of the fractional-order fuzzy neural networks. Two numerical examples are also given to illustrate the feasibility and effectiveness of the obtained results.     

Input-to-state stability of impulsive inertial memristive neural networks with time-varying delayed

The property of input-to-state stability (ISS) of inertial memristor-based neural networks with impulsive effects is studied. Firstly, according to the characteristics of memristor and inertial neural networks, the inertial memristor-based neural networks are built. Secondly, based on the impulsive control theory, the average impulsive interval approach, Halanay differential inequality, Lyapunov method and comparison property, some sufficient conditions ensuring ISS of the inertial memristor-based neural networks under impulsive controller are derived. In this paper, we consider two types of impulse, stabilizing impulses and destabilizing impulses. When the inertial memristor-based neural networks are originally not ISS, by choosing a suitable lower bound of the average impulsive interval, the stabilizing impulses can be used to stabilize the inertial memristor-based neural networks. On the contrary, the inertial memristor-based neural networks are originally ISS, by restricting the upper bound of the average impulsive interval, the ISS of inertial memristor-based neural networks with destabilizing impulses can be ensured. Finally, numerical results are presented to illustrate the main results.     

Robust exponential stability of uncertain impulsive stochastic neural networks with delayed impulses

In this paper, by using Lyapunov functions, Razumikhin techniques and stochastic analysis approaches, the robust exponential stability of a class of uncertain impulsive stochastic neural networks with delayed impulses is investigated. The obtained results show that delayed impulses can make contribution to the stability of system. Compared with existing results on related problems, this work improves and complements ones from some works. Two examples are discussed to illustrate the effectiveness and the advantages of the results obtained.     

Event-triggered synchronization of delayed neural networks with actuator saturation using quantized measurements

This paper studies event-triggered synchronization control problem for delayed neural networks with quantization and actuator saturation. Firstly, in order to reduce the load of network meanwhile retain required performance of system, the event-triggered scheme is adopted to determine if the sampled signal will be transmitted to the quantizer. Secondly, a synchronization error model is constructed to describe the master-slave synchronization system with event-triggered scheme, quantization and input saturation in a unified framework. Thirdly, on the basis of Lyapunov–Krasovskii functional, sufficient conditions for stabilization are derived which can ensure synchronization of the master system and slave system; particularly, a co-designed parameters of controller and the corresponding event-triggered parameters are obtained under the above stability condition. Lastly, two numerical examples are employed to illustrate the effectiveness of the proposed approach.     

Exponential stability of delayed neural networks with delayed sampled-data inputs: An extended bilateral looped functional approach

In this paper, the exponential stability of delayed neural networks (DNNs) with delayed sampled-data inputs is investigated via extended bilateral looped functional approach. Firstly, a new extended bilateral looped functional is constructed, which is differentiable at sampling intervals and can relax the constraints on positive definiteness when compared to traditional functionals. Then, less conservative criteria for exponential stability of DNNs with delayed sampled-data inputs expressed through linear matrix inequalities (LMIs) are obtained. Furthermore, the results are extended to T–S fuzzy DNNs with delayed sampled-data inputs, where corresponding stability conditions are likewise derived. Finally, two simulation examples are given to illustrate the validity of the main results.     

Improved exponential stability analysis for delayed recurrent neural networks

In this paper, we investigate the problem of global exponential stability analysis for a class of delayed recurrent neural networks. This class includes Hopfield neural networks and cellular neural networks with interval time-delays. Improved exponential stability condition is derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria are delay dependent and characterized by linear matrix inequalities (LMIs). The developed results are less conservative than previous published ones in the literature, which are illustrated by representative numerical examples.     

Design of memory controllers for finite-time stabilization of delayed neural networks with uncertainty

In this paper, we investigate the problem of finite-time stabilization of time-varying delayed neural networks with uncertainty. By employing the Lyapunov approach and linear matrix inequalities (LMIs), two different memory controllers are derived to achieve the finite-time stabilization of the addressed neural networks. Moreover, the upper bound of the setting-time for stabilization can be estimated via different Lyapunov functions. Our results improve and extend some recent works. Finally, the effectiveness and feasibility of the proposed controllers are demonstrated by numerical simulations.     

Stabilization of time delay systems with saturations via PDE predictor boundary control design

This paper addresses the stabilization issue of linear time delay system with input saturation and distinct input delays via predictor feedback boundary control algorithm by employing transport partial differential equations (PDEs). First, the addressed ordinary differential equation (ODE) system with input delay is equivalently represented as a cascade of an ODE and transport PDEs. Second, by employing the backstepping Volterra integral transformation technique, the equivalent cascade system is transformed into a stable target system, whose kernels are solved by the constraints satisfying transport PDEs. Third, based on the boundary conditions of the obtained invertible transformation, the proposed feedback control law can be formulated. Fourth, by applying semigroup operator theory, the well-posedness of the resulting system is proved and consequently, novel exponential stability conditions of the addressed system are established. Then, the domain of attraction region under the given actuator saturation constraints is estimated with the help of the solution of obtained stability conditions. Finally, a demonstrative simulation example is offered to verify the feasibility and usefulness of the results.     

Aperiodically intermittent control for synchronization of discrete-time delayed neural networks

This paper is concerned with the aperiodically intermittent control (AIC) for the synchronization of discrete-time neural networks with time delay. The synchronization is analyzed by the piecewise Lyapunov function approach and the piecewise Lyapunov–Krasovskii functional approach, respectively. The average activation time ratio of AIC is estimated, which is more general and less conservative than the minimum activation time ratio. Finally, a numerical example is exploited and detailed comparisons are presented to demonstrate the effectiveness and less conservativeness of the obtained results.     

Stabilization of the stochastic jump diffusion systems by state-feedback control

This paper addresses the stabilization of stochastic jump diffusion system in both almost sure and mean square sense by state-feedback control. We find conditions under which the solutions to the class of jump-diffusion process are mean square exponentially stable and almost sure exponentially stable. We investigate the stabilization of the stochastic jump diffusion systems by applying the state-feedback controllers not only in the drift term, but also in jump diffusion terms. Meanwhile our theory is generalized to cope with the uncertainty of system parameters. All the results are expressed in terms of linear matrix inequalities (LMIs), which are easy to be checked in a MATLAB Toolbox.     

Global synchronization in nonlinearly coupled delayed memristor-based neural networks with excitatory and inhibitory connections

This investigation establishes the global synchronization of an array of coupled memristor-based neural networks with delays. The coupled networks that are considered can incorporate both the internal delay in each individual network and the transmission delay across different networks. The coupling scheme, which consists of a nonlinear term and a sign term, is rather general. In particular, it can be asymmetric, and admits the coexistence of excitatory and inhibitory connections. Based on an iterative approach, the problem of synchronization is transformed into solving a corresponding linear system of algebraic equations. Subsequently, the respective synchronization criteria, which depend on whether the transmission delay exists, are derived respectively. Three examples are given to illustrate the effectiveness of the theories presented in this paper. The synchronization of the systems in two examples cannot be handled by existing techniques.     

On the exponential stabilization of a flexible structure with dynamic delayed boundary conditions via one boundary control only

This study deals with the stability analysis of a flexible structure with one and only one boundary control. The system is composed of three parts: a cart (motorized platform), a flexible cable, and a load mass attached to the lower part of the cable. This situation leads to a hybrid system as a mathematical model for the cable dynamics: one partial differential equation coupled to two ordinary differential equations. Despite the presence of a time-delay in the top-end of the cable, we are able to prove that the hybrid system is well-posed in the sense of semigroups theory and more importantly, only one boundary control can guarantee the exponentially decay of the energy of the system under reasonable conditions on the parameters of the system. This outcome considerably improves the result recently established in [17], where two more controls are required: one interior (Kelvin–Voigt) damping which acts over the entire cable and another boundary control which is exerted on the lower-end of the cable. Furthermore, we provide an estimate of the exponential decay of the system by means an appropriate Lyapunov functional. Lastly, numerical examples are presented in order to ascertain and highlight our theoretical outcomes.     

Stability of delayed Hopfield neural networks under a sublinear expectation framework

In this paper, we consider the stability of a class of stochastic delay Hopfield neural networks driven by G-Brownian motion. Under a sublinear expectation framework, we give the definition of exponential stability in mean square and construct some conditions such that the stochastic system is exponentially stable in mean square. Moreover, we also consider the stability of the Euler numerical solution of such equation. Finally, we give an example and its numerical simulation to illustrate our results.     

Finite-time lag projective synchronization of nonidentical fractional delayed memristive neural networks

Without considering identical systems, this paper investigates the finite-time lag projective synchronization of nonidentical fractional delayed memristive neural networks (FDMNN) by designing a novel fractional sliding mode controller (SMC). Due to the existence of memristor, the research is under the framework of Filippov solution. We firstly construct a fractional integral sliding mode surface (SMS). Based on sliding mode control theory and Lyapunov stability theorem, a novel fractional SMC is proposed to realize the lag projective synchronization of nonidentical FDMNN in finite time, and the synchronization setting time is less conservative than the existing results. As the special cases, some sufficient conditions are extended to projective synchronization, lag synchronization, anti-lag synchronization of nonidentical FDMNN in finite time, which improve and enrich some existing results. At last, a simulation example is given to prove the validity of the conclusions.     

New gain-scheduling control conditions for time-varying delayed LPV systems

This paper investigates the problem of stability and state-feedback control design for linear parameter-varying systems with time-varying delays. The uncertain parameters are assumed to belong to a polytope with bounded known variation rates. The new conditions are based on the Lyapunov theory and are expressed through Linear Matrix Inequalities. An alternative parameter-dependent Lyapunov-Krasovskii functional is employed and its time-derivative is handled using recent integral inequalities for quadratic functions proposed in the literature. As main results, a novel sufficient stability condition for delay-dependent systems as well as a new sufficient condition to design gain-scheduled state-feedback controllers are stated. In the new proposed methodology, the Lyapunov matrices and the system matrices are put separated making it suitable for supporting in a new way the design of the stabilization controller. An example, based on a model of a real-world problem, is provided to illustrate the effectiveness of the proposed method.     

Stabilization of periodic oscillations with transient delayed feedback control

Transient delayed feedback control is proposed by applying the transient control technique to the original delayed feedback control, with the aim of enlarging the stable region of the stabilized periodic oscillation, where the stable region is a subset of the parameter space of feedback gains for which the periodic oscillation is stabilized. The control signal is activated when the system is in a certain subset (the controlling area) of the state space, and inactivated otherwise, which is different from the standard control signal of the original delayed feedback control. The specific control performances of the transient delayed feedback control are investigated through case studies. The relationship between the stable region of the stabilized periodic oscillation and the controlling area is obtained by calculating the maximum Lyapunov exponent, which is a function of the feedback gain. It is shown that the stable region varies non-smoothly with the change of the controlling area. When the controlling area is properly chosen, the stable region with transient delayed feedback control is much larger than the stable region with original delayed feedback control.     

Intermittent fault detection for delayed stochastic systems over sensor networks

This paper is concerned with the intermittent fault (IF) detection problem for a class of linear discrete-time stochastic systems over sensor networks with constant time delay. By utilizing the lifting method, the distributed decoupled observers are proposed based on the output information of neighbor nodes and the node itself. In order to detect the appearing time and disappearing time of the IF, the truncated residuals are designed by introducing a sliding-time window. Furthermore, the IF detection and location thresholds are determined based on the hypothesis testing technique and the detectability of the IF is analyzed in the framework of stochastic analysis. Finally, a simulation example is presented to illustrate the effectiveness of the derived results.