Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay

This paper proposes new delay-dependent synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay. By constructing a suitable Lyapunov–Krasovskii's functional and utilizing Finsler's lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by using the LMI toolbox in MATLAB. Three numerical examples are given to illustrate the effectiveness of the proposed methods.     

Synchronization control of neutral-type neural networks with sampled-data via adaptive event-triggered communication scheme

This paper addresses the problem of synchronization control of neutral-type neural networks with sampled-data, where sampled data will be over a communication network before received by controller. Generally, the communication network is with a bandwidth-limited communication channel. To reduce network burden, an event-triggered scheme is designed between the sampler and communication network. A weak synchronization conditions are derived by using our proposed integral inequality. Finally, a numerical example is given to illustrate the effectiveness and advantage of the proposed results.     

Existence and global stability analysis of equilibrium of fuzzy cellular neural networks with time delay in the leakage term under impulsive perturbations

This paper considers existence, uniqueness and the global asymptotic stability of fuzzy cellular neural networks with mixed delays. The mixed delays include constant delay in the leakage term (i.e., “leakage delay”), time-varying delays and continuously distributed delays. Based on the Lyapunov method and the linear matrix inequality (LMI) approach, some sufficient conditions ensuring global asymptotic stability of the equilibrium point are derived, which are dependent on both the discrete and distributed time delays. These conditions are expressed in terms of LMI and can be easily checked by MATLAB LMI toolbox. In addition, two numerical examples are given to illustrate the feasibility of the result.     

Exponential synchronization of the switched uncertain neural networks with mixed delays based on sampled-data control

The synchronization for a class of switched uncertain neural networks (NNs) with mixed delays and sampled-data control is researched in this paper. When a switching signal occurs in a sampling interval, the controller cannot switch until the next sampling instant. There is a mismatch between the system and the controller. Thus, we devise the control strategy to guarantee that the switched NNs can be synchronized. The proposed Lyapunov-Krasovskii functional (LKF) can make full use of system information. By use of an improved integral inequality, some sufficient stability conditions formed by linear matrix inequalities (LMIs) are derived for the synchronization of switched NNs. Average dwell time (ADT) is obtained as a form of inequality that includes the sampling interval. At last, the feasibility of the proposed method is proved by some numerical examples.     

Synchronization of stochastic perturbed chaotic neural networks with mixed delays

In this paper, we study the synchronization problem of a class of chaotic neural networks with time-varying delays and unbounded distributed delays under stochastic perturbations. By using Lyapunov-Krasovskii functional, drive-response concept, output coupling with delay feedback and linear matrix inequality (LMI) approach, we obtain some sufficient conditions in terms of LMIs ensuring the exponential synchronization of the addressed neural networks. The feedback controllers can be easily obtained by solving the derived LMIs. Moreover, the main results are generalizations of some recent results reported in the literature. A numerical example is also provided to demonstrate the effectiveness and applicability of the obtained results.     

Global dissipativity of stochastic neural networks with time delay

Liao and Wang [Global dissipativity of continuous-time recurrent neural networks with time delay, Phys. Rev. E 68 (2003) 016118] firstly studied the dissipativity of neural networks. In this paper, the neural network model is generalized to a stochastic case, and the global dissipativity in mean of such stochastic system is investigated. By constructing several proper Lyapunov functionals combining with Jensen's inequality, Itô's formula and some analytic techniques, several sufficient conditions for the global dissipativity in mean of such stochastic neural networks are derived in LMIs forms, which can be easily verified in practice. Three numerical examples are provided to demonstrate the effectiveness of our criteria.     

Non-fragile exponential synchronization of delayed complex dynamical networks with transmission delay via sampled-data control

This paper is devoted to the non-fragile exponential synchronization problem of complex dynamical networks with time-varying coupling delays via sampled-data static output-feedback controller involving a constant signal transmission delay. The dynamics of the nodes contain s quadratically restricted nonlinearities, and the feedback gain is allowed to have norm-bounded time-varying uncertainty. The control design is based on a Lyapunov–Krasovskii functional, which consists of the sum of terms assigned to the individual nodes, i.e., it is constructed without merging the complex dynamical network’s nodes into a single large-scale system. In this way, the proposed design method has substantially reduced computational complexity and improved conservativeness, and guaranties non-fragile exponential stability of the error system. The sufficient stability condition is expressed in terms of linear matrix inequalities that are solvable by standard tools. The efficiency of the proposed method is illustrated by numerical examples.     

Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control

This paper investigates the stability and stabilizability of complex-valued memristive neural networks (CVMNNs) with random time-varying delays via non-fragile sampled-data control. Taking the influence of gain fluctuations into account, a non-fragile sampled-data controller is designed for CVMNNs. Compared with the existing control schemes, the one here is more applicable and can effectively save the communication resources. The assumption on activation functions of CVMNNs is relaxed by only needing the complex-valued activation functions satisfying the Lipschitz condition. By constructing a suitable Lyapunov–Krasovskii functional (LKF), new stability and stabilizability criteria are derived for CVMNNs. Different from the existing results with the maximum absolute values of memristive connection weights, our ones are based on the average values of the maximum and minimum of the memristive connection weights. Finally, numerical simulations are given to validate the effectiveness of the theoretical results.     

Stability analysis of stochastic fuzzy Markovian jumping neural networks with leakage delay under impulsive perturbations

This paper investigates the global asymptotic stability of stochastic fuzzy Markovian jumping neural networks with mixed delays under impulsive perturbations in mean square. The mixed delays include constant delay in the leakage term (i.e., “leakage delay”), time-varying delay and continuously distributed delay. By using the Lyapunov functional method, reciprocal convex approach, linear convex combination technique, Jensen integral inequality and the free-weight matrix method, several novel sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point of the considered networks in mean square. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab software. Finally, two numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over existing literature.     

Synchronization of complex networks with hybrid delays based on event intermittent control

This paper studies the exponential synchronization problem for complex networks with hybrid delays via means of the event intermittent control (EIC) strategy. Compared with the traditional aperiodically intermittent control, the control instants and the rest instants are generated when the events occur, which is more in accordance with the actual situation. An modified lemma related to delays is derived without predesigning intermittent instants. Furthermore, some synchronization criteria with less conservatism are established in terms of linear matrix inequalities (LMIs). Meanwhile, it is also shown that Zeno behavior is excluded. Finally, simulations of a numerical example are given to verify the effectiveness of the proposed EIC strategy.     

Lag synchronization for fractional-order memristive neural networks with time delay via switching jumps mismatch

This paper studies drive-response synchronization in fractional-order memristive neural networks (FMNNs) with switching jumps mismatch. A comparison theorem for fractional-order systems with variable order is provided first. Theories of fractional order Filippov differential inclusions are used to treat FMNNs because the parameters of FMNNs are state dependent and the FMNNs has discontinuous right hand sides. Based on Laplace transform and linear feedback control, some lag quasi-synchronization conditions are obtained with variable order α: 0?<?α?<?1 and 1?<?α?<?2. The error level is estimated and the larger synchronization regain is discussed. Finally, two numerical examples are presented to illustrate the effectiveness of our proposed theorems.     

Synchronization of IT2 stochastic fuzzy complex dynamical networks with time-varying delay via fuzzy pinning control

In this paper, the problem of synchronization on interval type-2 (IT2) stochastic fuzzy complex dynamical networks (CDNs) with time-varying delay via fuzzy pinning control is fully studied. Firstly, a more general complex network model is considered, which involves the time-varying delay, IT2 fuzzy and stochastic effects. More specifically, IT2 fuzzy model, as a meaningful fuzzy scheme, is investigated for the first time in CDNs. Then, with the aid of Lyapunov stability theory and stochastic analysis technique, some new sufficient criteria are established to ensure synchronization of the addressed systems. Moreover, on basis of the parallel-distributed compensation (PDC) scheme, two effective fuzzy pinning control protocols are proposed to achieve the synchronization. Finally, a numerical example is performed to illustrate the effectiveness and superiority of the derived theoretical results.     

Existence and exponential stability of almost periodic solution for neutral delay BAM neural networks with time-varying delays in leakage terms

This paper is concerned with a class of neutral delay BAM neural networks with time-varying delays in leakage terms. Some sufficient conditions are established to ensure the existence and exponential stability for such class of neural networks by employing the exponential dichotomy of linear differential equations, fixed point theorems and differential inequality techniques. An example is provided to show the effectiveness of the theoretical results. The results of this paper are completely new and complementary to the previously known results.     

Discrete-time stochastic impulsive BAM neural networks with leakage and mixed time delays: An exponential stability problem

In this paper, the stability analysis of impulsive discrete-time stochastic BAM neural networks with leakage and mixed time delays is investigated via some novel Lyapunov–Krasoviskii functional terms and effective techniques. For the target model, stochastic disturbances are described by Brownian motion. Then the result is further extended to address the problem of robust stability of uncertain discrete-time BAM neural networks. The conditions obtained here are expressed in terms of Linear Matrix Inequalities (LMIs), which can be easily checked by MATLAB LMI control toolbox. Finally, few numerical examples are presented to substantiate the effectiveness of the derived LMI-based stability conditions.     

Adaptive finite-time tracking control of robot manipulators with multiple uncertainties based on a low-cost neural approximator

Benefiting from a newly designed switching function in terminal sliding manifold and novel uncertainty handling solutions, this article presents a low-cost neuroadaptive control scheme that can not only achieve the finite time tracking control of robot system with multiple uncertainties also circumvent the possible singularity. Specifically, for the kinematics parameter uncertainties involved, the proposed terminal sliding mode observer can ensure the actual position of end-effector be accurately estimated within a finite time. And then, a neural approximator is designed to handle the non-parameterizable lumped dynamics uncertainty, and a new low-cost neural adaptive mechanism is constructed to reduce the computational burden. Furthermore, it is proved that all closed-loop signals are bounded and the tracking error converges to an arbitrarily small adjustable neighborhood of the origin within a finite time. The comparison simulation example also confirms the effectiveness and superiority of the proposed control scheme.     

Improving the performance of networked control systems with time delay and data dropouts based on fuzzy model predictive control

This paper proposes a fuzzy model predictive control (FMPC) combined with the modified Smith predictor for networked control systems (NCSs). The network delays and data dropouts are problems, which greatly reduce the controller performance. For the proposed controller, the model of the controlled system is identified on-line using the Takagi – Sugeno (T-S) fuzzy models based on the Lyapunov function. There are two internal loops in the proposed structure. The first is the loop around the FMPC, which predicts the future outputs. The other is the loop around the plant to give the error between the system model and the actual plant. The proposed controller is designed for controlling a DC servo system through a wireless network to improve the system response. The practical results based on MATLAB/SIMULINK are established. The practical results are indicated that the proposed controller is able to respond the networked time delay and data dropouts compared to other controllers.     

Synchronization of chaotic systems with unknown parameters using adaptive passivity-based control

This paper investigates the problem of complete synchronization of chaotic systems with unknown parameters. An adaptive control scheme based on a feedback passivity approach is proposed. The convergence of the synchronization error is guaranteed. The unified chaotic and hyperchaotic Lü systems are taken as illustrative examples. The feasibility and effectiveness of the proposed scheme are demonstrated through numerical simulations.     

Synchronization control for multiple heterogeneous robotic systems with parameter uncertainties and communication delays

This paper investigates synchronization of multiple heterogeneous robotic systems (MHRSs) in the presence of kinematic uncertainties, dynamic uncertainties and communication delays. We develop two classes of adaptive sliding-mode controllers to deal with the aforementioned problem based on directed graphs containing a directed spanning tree, which relaxes the constraints on the interaction topologies. Furthermore, by invoking techniques of Lyapunov and input-output stability theories, we obtain the sufficient conditions on asymptotic stability of MHRSs. Thus, the position and velocity synchronization errors can asymptotically converge to the origin. Finally, numerous simulations are carried out to demonstrate the validity and advantages of the theoretical results.