Wuneng Zhou

Mean square exponential synchronization in Lagrange sense for uncertain complex dynamical networks

Abstract In this paper, the problem of the mean square exponential synchronization in Lagrange sense for the uncertain complex network is investigated. A complex network usually appears some uncertain phenomena, which includes varying topology structure, destroyed nodes, and the noise disturbance from circumstance. Based on the Lyapunov stability theory and the Kronecker product analysis technique, some conditions to guarantee the complex network mean square exponential synchronization in Lagrange sense are provided. Finally, two numerical examples are provided to illustrate the effectiveness of the method proposed.

Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control

In this paper, the adaptive exponential synchronization problem of neutral-type coupled neural networks with Markovian switching parameters is investigated. The switching parameters are modeled as a continuous time, finite state Markov chain. Based on Lyapunov stability theory, stochastic analysis and matrix theory, some sufficient conditions for exponential synchronization in mean square are derived. The adaptive controllers are added to part of nodes, and the adaptive laws are depend on Markov chain and error states. Two numerical examples are exhibited to illustrate the validity of the theoretical results. Through the comparison of average value of synchronization control cost and synchronization time, we verify that control different nodes may be more effectively to achieve synchronization than control fixed nodes when the network topology is switching by a Markov chain.

Finite-time state estimation for delayed Hopfield neural networks with Markovian jump

In this paper, the finite-time state estimation problem of delayed Hopfield neural networks with Markovian jump is investigated. The activation functions are assumed to satisfy the section condition. A discontinuous estimator is designed through available output measurements such that the estimation error converges to the origin in finite time. The conditions that the desired estimator parameters need to satisfy are derived by using the Lyapunov stability theory and inequality technique. These conditions are provided in terms of the linear matrix inequalities. Finally, the effectiveness of the proposed method is illustrated by means of a numerical example. HighlightsThe finite-time state estimation of delayed Hopfield neural networks with Markovian jump is considered.The convergence time can be adjusted by tuning the estimator parameters.The results are presented in terms of the linear matrix inequalities.

Finite-time master-slave synchronization and parameter identification for uncertain Lurie systems.

This paper investigates the finite-time master-slave synchronization and parameter identification problem for uncertain Lurie systems based on the finite-time stability theory and the adaptive control method. The finite-time master-slave synchronization means that the state of a slave system follows with that of a master system in finite time, which is more reasonable than the asymptotical synchronization in applications. The uncertainties include the unknown parameters and noise disturbances. An adaptive controller and update laws which ensures the synchronization and parameter identification to be realized in finite time are constructed. Finally, two numerical examples are given to show the effectiveness of the proposed method.

Almost surely exponential stability of neural networks with Lévy noise and Markovian switching

Abstract In this brief, the problem of almost surely exponential stability analysis is considered for neural networks with Levy noise and Markovian switching. The switching parameters are generated from a continuous-time irreducible Markov chain taking value in a finite-state space. The purpose of the problem addressed is to derive a sufficient condition such that the dynamics of the neural network is almost surely exponentially stable. By generalized Ito׳s formula, strong law of large numbers for martingales and ergodicity of Markov chain, the stochastic analysis approach is developed to establish the desired condition which depends only on the stationary distribution of the Markov chain and some constants. Two numerical examples are given to verify the usefulness of the stability condition.

Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation

In this paper, the problem of adaptive synchronization for time-delay neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic noise is researched. The adaptive synchronization model which contains a drive system and multiple response systems is presented. By using of the generalized It o ^ s formula and the M-matrix method, the sufficient condition is obtained to guarantee that the error control system is stable, and the update law of the feedback controller is designed to ensure that every slave system synchronizes with master system. Finally, a numerical example is given to illustrate the effectiveness of the method and result obtained in this paper.

Robust master-slave synchronization for general uncertain delayed dynamical model based on adaptive control scheme

In this paper, the robust exponential synchronization problem for a class of uncertain delayed master-slave dynamical system is investigated by using the adaptive control method. Different from some existing master-slave models, the considered master-slave system includes bounded unmodeled dynamics. In order to compensate the effect of unmodeled dynamics and effectively achieve synchronization, a novel adaptive controller with simple updated laws is proposed. Moreover, the results are given in terms of LMIs, which can be easily solved by LMI Toolbox in Matlab. A numerical example is given to illustrate the effectiveness of the method.

Mode-dependent projective synchronization for neutral-type neural networks with distributed time-delays

Abstract This paper investigates the mode-dependent projective synchronization problem of a couple of stochastic neutral-type neural networks with distributed time-delays. By using the Lyapunov stability theory and the adaptive control method, a sufficient projective synchronization criterion for this neutral-type neural network model is derived. A numerical simulation is exploited to illustrate the feasibility and effectiveness of the theoretical result.

Target-synchronization of the distributed wireless sensor networks under the same sleeping–awaking method

Abstract Based on the conflict and crosstalk avoidance mechanism (CCAM), we propose a sleeping–awaking method for wireless sensor networks (WSNs) in which the maximal degree node (MDN) and all its neighbors run sleep or wake simultaneously while other nodes run the CCAM. This method is said to be the same sleeping–awaking method (SSAM). The SSAM is motivated by the congestion and collision problems of cliques, MDN and its neighbor set in the communicating graph of the WSN. In this communication way, the related protocol about the SSAM is provided accordingly. Under the designed protocol, we get a Markovian switching WSN with both white noise disturbance and multiple time-varying delays. Based on the theory of exponential stability in p th moment, we show that the protocol ensures the WSNs to keep in synchronization with the target function. A numerical example shows that the WSN can keep its target-synchronization even with large time delays.

Asymptotical stability of stochastic neural networks with multiple time-varying delays

The stochastic neural networks can be considered as an expansion of cellular neural networks and Hopfield neural networks. In real world, the neural networks are prone to be instable due to time delay and external disturbance. In this paper, we consider the asymptotic stability for the stochastic neural networks with multiple time-varying delays. By employing a Lyapunov-Krasovskii function, a sufficient condition which guarantees the asymptotic stability of the state trajectory in the mean square is obtained. The criteria proposed can be verified readily by utilising the linear matrix inequality toolbox in Matlab, and no parameters to be tuned. In the end, two numerical examples are provided to demonstrate the effectiveness of the proposed method.