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1.
《Journal of The Franklin Institute》2022,359(18):11108-11134
This paper focuses on the stochastic passivity problem of stochastic memristor-based complex valued neural networks with two different types of time-delays and reaction-diffusion terms by sampled-data control strategy. Different from the existing sampled-data strategies, this paper develops spatial and temporal point sampling, namely, only a finite number of points in space or time are sampled. By introducing two different Lyapunov functional and employing techniques such as Wirtinger’s integral inequality, Jensen’s inequality and Young’s inequality, etc., two different sufficient conditions for the stochastic passivity of the system are established. Prominently, the condition quantitatively reveals the relationship between the upper and lower bounds of the sampling interval at spatial and temporal points. Finally, a numerical example is given to verify the rationality of the proposed method. Notice, compared with a large number of results of real-valued reaction-diffusion neural networks, the research results of sampled-data controlled complex-valued reaction-diffusion neural networks have not appeared so far, and this work is the first attempt to fill in the gaps in this topic.  相似文献   

2.
This paper is concentrated on exploring the exponential synchronization of reaction-diffusion coupled neural networks with fractional-order and impulses. Firstly, an extended Halanay-type inequality is established to cope with the hybrid delay-dependent impulsive problem by utilizing the mathematical induction. Furthermore, a direct error method is introduced by constructing Lyapunov function for the addressed networks to investigate the exponential synchronization under impulsive effects. By utilizing the technique of average impulsive interval and strength, some sufficient synchronization criteria are derived, which are closely associated with time delay and the commensurate order for fractional-order systems. Lastly, three numerical examples are presented to demonstrate the correctness for established results.  相似文献   

3.
《Journal of The Franklin Institute》2023,360(13):10080-10099
In this paper, the quasi-synchronization problem of heterogeneous stochastic coupled neural networks (HSCNNs) is discussed. The effects of the mixed time-varying delay and diffusion phenomenon on the system are considered separately in time and space. Moreover, different from the previous distributed control, boundary control is introduced to realize network synchronization. This not only reduces the space cost of the controller, but also makes it easier to implement. Thus, the mean-square quasi-synchronization of HSCNNs is guaranteed by using matrix inequality and stochastic analysis tools. In addition to focusing on systems with Neumann boundary conditions, we briefly investigate HSCNNs with time-invariant delays and mixed boundary conditions respectively, and provide sufficient conditions to achieve the desired performance. Finally, the correctness of the conclusion is verified by several examples.  相似文献   

4.
This paper investigate the generalized synchronization and pinning adaptive generalized synchronization for delayed coupled different dimensional neural networks with hybrid coupling, respectively. First, some sufficient conditions for reaching the generalized synchronization and pinning generalized synchronization of the considered network are acquired by using some inequality techniques and Lyapunov functional method. Second, because the precise parameter values of network cannot be obtained in some situations, we also purse the study on the generalized synchronization analysis and pinning control for the case of coupled different dimensional neural networks with parameter uncertainties. Third, two numerical examples are provided for substantiating the effectiveness of the derived results.  相似文献   

5.
In this paper, the global exponential robust stability is investigated for Cohen-Grossberg neural network with time-varying delays and reaction-diffusion terms, this neural network contains time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. Neither the boundedness and differentiability on the activation functions nor the differentiability on the time-varying delays are assumed. By using general Halanay inequality and M-matrix theory, several new sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential robust stability of equilibrium point for Cohen-Grossberg neural network with time-varying delays and reaction-diffusion terms. Several previous results are improved and generalized, and three examples are given to show the effectiveness of the obtained results.  相似文献   

6.
This paper is concerned with the stochastic synchronization problem for a class of Markovian hybrid neural networks with random coupling strengths and mode-dependent mixed time-delays in the mean square. First, a novel inequality is established which is a double integral form of the Wirtinger-based integral inequality. Next, by employing a novel augmented Lyapunov–Krasovskii functional (LKF) with several mode-dependent matrices, applying the theory of Kronecker product of matrices, Barbalat’s Lemma and the auxiliary function-based integral inequalities, several novel delay-dependent conditions are established to achieve the globally stochastic synchronization for the mode-dependent Markovian hybrid coupled neural networks. Finally, a numerical example with simulation is provided to illustrate the effectiveness of the presented criteria.  相似文献   

7.
This paper is devoted to investigating the robust stochastic exponential stability for reaction-diffusion Cohen–Grossberg neural networks (RDCGNNs) with Markovian jumping parameters and mixed delays. The parameter uncertainties are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Some criteria for delay-dependent robust exponential stability of RDCGNNs with Markovian jumping parameters are established in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing Matlab LMI toolbox. Numerical examples are provided to demonstrate the efficiency of the proposed results.  相似文献   

8.
This paper deals with the exponential boundary stabilization for a class of Markov jump reaction-diffusion neural networks (MJRDNNs) with mixed time-varying delays, which is described by T-S fuzzy model. It is assumed that observed modes in boundary controller are not synchronized with the system modes. Based on a hidden Markov model (HMM), a novel asynchronous boundary control law is developed by using observed modes. Compared with the existing control strategies for distributed parameter systems, the asynchronous boundary control scheme can not only save the cost of the controller installation, but also bring less conservativeness. A delay-dependent sufficient condition to guarantee the exponentially mean square stability is established for T-S fuzzy MJRDNNs with mixed time-varying delays by constructing a Lyapunov functional and utilizing the vector-value Wirtinger-type inequality. Meanwhile, in order to get the designing scheme of the boundary controller, an equivalent LMI-based sufficient criterion is established. In the end, the effectiveness of the proposed results is illustrated by simulation examples.  相似文献   

9.
This work realizes lag quasi-synchronization of incommensurate fractional-order memristor-based neural networks (FMNNs) with nonidentical characteristics via quantized control. The motivations behind this research work are threefold: (1) quantized controllers, which generate discrete control signals, can be more easily realized in computers than non-quantized controllers, and can consume smaller communication capacity; (2) incommensurate orders in a single FMNN and nonidentical characteristics in drive-response FMNNs are inescapable due to the differences among the circuit elements used to implement FMNNs; (3) convergence analysis of delayed incommensurate fractional-order nonlinear systems, which is the basis for the derivation of synchronization criterion, has not been handled perfectly. As an effective tool for convergence analysis of delayed incommensurate fractional-order nonlinear systems, especially for estimation of ultimate state bound, a vector fractional Halanay inequality is established at first. Then, a quantized synchronization controller, in which the dead-zone is introduced into some logarithmic quantizers to avoid chattering phenomenon, is designed. By means of vector Lyapunov function together with the newly derived vector fractional Halanay inequality, the synchronization criterion is proved theoretically. Lastly, numerical simulations supplementarily illustrate the correctness of the synchronization criterion. In contrast with the hypotheses in the relevant literature, the hypotheses in this paper are weaker.  相似文献   

10.
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.  相似文献   

11.
In this paper, we concern the finite-time synchronization problem for delayed dynamical networks via aperiodically intermittent control. Compared with some correspondingly previous results, the intermittent control can be aperiodic which is more general. Moreover, by establishing a new differential inequality and constructing Lyapunov function, several useful criteria are derived analytically to realize finite-time synchronization for delay complex networks. Additionally, as a special case, some sufficient conditions ensuring the finite-time synchronization for a class of coupled neural network are obtained. It is worth noting that the convergence time is carefully discussed and does not depend on control widths or rest widths for the proposed aperiodically intermittent control. Finally, a numerical example is given to demonstrate the validness of the proposed scheme.  相似文献   

12.
《Journal of The Franklin Institute》2021,358(18):10052-10078
This paper is concerned with the fixed-time quasi-synchronization of coupled memristive neural networks (CMNNs). The communication channel is subject to the deception attack described by the Bernoulli stochastic variable. To reduce signal transmissions, a dual-channel event-triggered mechanism is proposed. In each channel of sensor to controller and controller to actuator, an event-triggered mechanism is designed. Compared with the single event-triggered mechanism in the communication loop, the main difficulties lie in how to deal with the problems of packet scheduling and network attacks. By using Lyapunov method combining with a new proposed lemma, some sufficient conditions are derived to guarantee the leader-following quasi-synchronization of CMNNs. The Zeno behavior is excluded for the designed dual-channel event-triggered mechanism. The influence of the event-triggered mechanism on the estimation of settling time is discussed. Three numerical examples are provided to show the effectiveness of the theoretical results.  相似文献   

13.
The cluster synchronization issues are investigated for directed coupled inertial reaction-diffusion neural networks (CIRDNNs) with nonidentical nodes by imposing two effective pinning control. A novel Lyapunov-Krasovskii functional (LKF) is established to directly analyze the dynamic behavior of CIRDNNs and deal with reaction-diffusion term, inertia term and coupling term. Moreover, based on different desired cluster synchronization states including a set of un-decoupled trajectories and the particular solutions of the decoupled node systems, two class of synchronization criteria in view of algebraic inequalities are derived under two different communication topologies, respectively. Finally, two typical examples are given to verify the theoretical results.  相似文献   

14.
Passivity-based boundary control is considered for time-varying delay reaction-diffusion systems (DRDSs) with boundary input-output. By virtue of Lyapunov functional method and inequality techniques, sufficient conditions are obtained for input strict passivity and output strict passivity of DRDSs, respectively. When the parameter uncertainties appear in DRDSs, sufficient conditions are presented to guarantee the robust passivity. Moreover, we apply our theoretical results to the synchronization problem of coupled delay reaction-diffusion systems and get the criterion to ensure the asymptotic synchronization. Finally, numerical simulations are provided to show the validity of our theoretical results.  相似文献   

15.
In this paper we study stochastic stability of delayed recurrent neural networks with both Markovian jump parameters and nonlinear disturbances. Based on the Lyapunov stability theory, the properties of a Brownian motion, the generalized Itô's formula and linear matrix inequalities technique, some new delay-dependent conditions are derived to guarantee the stochastically asymptotic stability of the trivial solution or zero solution. In particular, the activation functions in this paper depend on Markovian jump parameters and they are more general than those usual Lipschitz conditions. Also, time delays proposed in this paper comprise both constant delays and time-varying delays. Moreover, the derivative of time delays is allowed to take any value. Therefore, the results obtained in this paper are less conservatism and generalize those given in the previous literature. Finally, two numerical examples and their simulations are used to show the effectiveness of the obtained results.  相似文献   

16.
《Journal of The Franklin Institute》2022,359(18):10813-10830
This paper studies the exponential synchronization of stochastic reaction-diffusion neural networks based on semi-linear parabolic partial integro-differential equations. Compared with the traditional coupling of states, spatial boundary coupling is designed in this paper. Two kinds of boundary coupling within Neumann boundary conditions are studied, one under the collocated boundary measurement form and the other under the distributed measurement form. Two sufficient conditions for the exponential synchronization using the two kinds of boundary coupling are respectively obtained. Examples are given to show the effectiveness of the proposed spatial boundary coupling.  相似文献   

17.
Multiplex networks involve different types of synchronization due to their complex spatial structure. How to control multiplex networks to achieve different types of synchronization is an interesting topic. This paper considers the fixed-time synchronization of multiplex networks under sliding mode control (SMC). Firstly, for realizing three types of synchronization of multiplex networks in a fixed time, a unified sliding mode surface (SMS) is established. After that, based on the theory of SMC, a sliding mode controller (SMCr) which is more intelligent and has a simpler form than those in the existing literature is put forward for multiplex networks. It can not only guarantee the emergence of sliding mode motion, but also can realize three different kinds of synchronization by adjusting some parameters or even one parameter of the controller. Based on some theories of fixed-time stability, this paper deduces several sufficient conditions for the trajectories of the system to reach the preset SMS in a fixed time, and derives some sufficient conditions for multiplex networks to realize three different types of fixed-time synchronization. At the same time, the settling time which can reveal what factors determine the fixed-time synchronization in multiplex networks is obtained. Finally, in numerical simulations, different chaotic systems are set for each layer of multiplex networks to represent the nodes of different layers, which can prove that the theoretical results are practical and effective.  相似文献   

18.
In this paper, the D-type iterative learning control (ILC) protocol based on the local neighbor information is designed to achieve tracking synchronization for linearly coupled reaction-diffusion neural networks in presence of time delay and iteration-varying switching topology under a repetitive environment. Firstly, based on non-collocated sensors and actuators network, the proposed D-type ILC update law can realize tracking synchronization by utilizing output tracking errors. Then, by virtue of the contraction mapping principle, the sufficient convergence conditions of tracking synchronization errors are presented under the fixed commutation topology. Subsequently, the synchronization conclusions are extended to the iteration-varying commutation topology scenario. Finally, two numerical examples are provided to verify the efficacy of the obtained results.  相似文献   

19.
The exponential stabilization of BAM reaction-diffusion neural networks with mixed delays is discussed in this article. At first, a general pinning impulsive controller is introduced, in which the control functions are nonlinear and the pinning neurons are determined by reordering the state error. Next, based on the designed control protocol and the Lyapunov–Krasovskii functional approach, some novel and useful criteria, which depend on the diffusion coefficients and controlling parameters, are established to guarantee the global exponential stabilization of the considered neural networks. Finally, the effectiveness of the proposed control strategy is shown by two numerical examples.  相似文献   

20.
This paper investigates the global dissipativity and quasi-synchronization of asynchronous updating fractional-order memristor-based neural networks (AUFMNNs) via interval matrix method. First, a new class of FMNNs named AUFMNNs is proposed for the first time, in which the switching jumps are asymmetric. In other words, each memristive connection weight is updated based on its own channel and hence the number of the subsystems increases significantly from 2n to 22n2. Under the framework of fractional-order differential inclusions, the proposed AUFMNNs can be regarded as a system with interval parameters. Then, the global dissipativity criterion is established by constructing appropriate Lyapunov function in combination with the estimates of 2-norm for interval matrices and some fractional-order differential inequalities. In addition, for drive-response AUFMNNs with mismatched parameters, the problem of quasi-synchronization is explored via linear state feedback control. It has been shown that complete synchronization between two AUFMNNs cannot be achieved via linear feedback control and that the synchronization error bound can be controlled within a relatively small level by selecting suitable control parameters. Finally, three numerical examples are given to demonstrate the effectiveness and the improvement of the obtained results.  相似文献   

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