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.     

Finite/fixed-time synchronization control of coupled memristive neural networks

Finite-time and fixed-time synchronization (FAFS) of coupled memristive neural networks (CMNNs) with discontinuous feedback functions are explored in this paper. Firstly, a more comprehensive stability theory is systematically established. Secondly, by designing adaptive feedback controller and discontinuous feedback controller, both finite-time and fixed-time synchronization can be realized through regulating the main control parameter. Thirdly, 1-norm and quadratic-norm Lyapunov functions are considered simultaneously in this article, while in estimating the settling time, the former one is more accurate than the latter one under the same synchronization criteria. Finally, in numerical simulation, the analysis and comparison of the proposed controllers are given to show the effectiveness of the corresponding results.     

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.     

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.     

Novel discontinuous control for exponential synchronization of memristive recurrent neural networks with heterogeneous time-varying delays

This paper investigates the exponential synchronization problem of memristive recurrent neural networks (MRNNs) with heterogeneous time-varying delays (HTVDs). First, a novel discontinuous feedback control is designed, in which a tunable scalar is introduced. The tunable scalar makes the controller more flexible in reducing the upper bound of the control gain. Based on this control scheme, the double integral term can be successfully used to construct the LKF. Second, New method for tackling memristive synaptic weights and new estimation technique are presented. Third, based on the LKF and estimation technique, synchronization criterion is derived. In comparison with existing results, the established criterion is less conservatism thanks to the double integral term of the LKF. Finally, numerical simulations are presented to validate the effectiveness and advantages of the proposed results.     

Impulsive control for passivity and exponential synchronization of coupled neural networks with multiple weights

This paper investigates the passivity and synchronization problems for two classes of multiple weighted coupled neural networks (MWCNNs) with or without time delays. Firstly, by utilizing an impulsive control strategy and some inequality techniques, several passivity criteria for MWCNNs with diverse dimensions of output and input are established. Then, based on the Lyapunov functional, some sufficient conditions to ensure the synchronization of MWCNNs via impulsive control are derived. In addition, combined with the comparison principle and the impulsive delay differential inequality, the global exponential synchronization of MWCNNs with time-varying delays is considered under impulsive control. Finally, two numerical examples illustrate the effectiveness of the obtained results.     

Stop and Go adaptive strategy for synchronization of delayed memristive recurrent neural networks with unknown synaptic weights

Although the drive-response synchronization problem of memristive recurrent neural networks (MRNNs) has been widely investigated, all the existing results are based on the assumption that the parameters of the drive system are known in prior, which are difficult to implement in real-life applications. In the present paper, a Stop and Go adaptive strategy is proposed to investigate the synchronization control of chaotic delayed MRNNs with unknown memristive synaptic weights. Firstly, by defining a series of measurable logical switching signals, a switched response system is constructed. Subsequently, by utilizing the logical switching signals, several suitable parameter update laws are proposed, then some different adaptive controllers are devised to guarantee the synchronization of unknown MRNNs. Since the parameter update laws are weighted by the logical switching signals, they will work or stop automatically with the switch of the unknown weights of drive system. Finally, two numerical examples with their computer simulations are provided to illustrate the effectiveness of the proposed adaptive synchronization schemes.     

Internet of health things encryption via master-slave synchronization for stochastic quaternion-valued neural networks

This paper investigates the problem of master-slave synchronization of stochastic quaternion-valued neural networks (SQVNNs) with mixed time-varying delays. A linear feedback controller is developed to explore the global synchronization of the proposed system by utilizing the complete information of the time-delay state. Sufficient conditions for synchronization of the proposed model are derived by constructing appropriate Lyapunov–Krasovskii functional by applying the master-slave synchronization method of master-slave and some integral inequality techniques. Finally, a corresponding numerical simulation is presented to demonstrate the accuracy of the theoretical results. This paper introduces a unique and efficient image encryption algorithm based on SQVNNs. This technique utilizes the solution set of SQVNNs to generate the high-level randomness secret keys to encrypt the source image. Finally, we conclude that the algorithm yields a source image cipher with excellent diffusion and confusion properties. A few test clinical images are utilized to show the validity of the proposed method. Several performance analyses show that the proposed algorithm for image encryption gives an efficient and secure way to deal with the Internet of Health Things (IoHT).     

Two boundary coupling approaches for synchronization of stochastic reaction-diffusion neural networks based on semi-linear PIDEs

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.     

Mode-dependent stochastic synchronization criteria for Markovian hybrid neural networks with random coupling strengths

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.     

Fixed-time dual-channel event-triggered secure quasi-synchronization of coupled memristive neural networks

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.     

Adaptive fixed-time synchronization of stochastic memristor-based neural networks with discontinuous activations and mixed delays

In this paper, adaptive fixed-time synchronization(FTS) of stochastic memristor-based neural networks(MNNs) with discontinuous activations and mixed delays is investigated. Both continuous and discontinuous activation functions are discussed for stochastic MNNs. Meanwhile, a feedback control strategy and a new adaptive control strategy are proposed to ensure FTS of stochastic MNNs. Since the MNNs are right-hand discontinuous systems, the set-valued mapping and differential inclusion theory are used to deal with its discontinuity. Synchronization criteria and the settling time (ST) are obtained with the aid of some lemmas and mathematical inequalities under corresponding control schemes. It’s worth noting that the ST can be adjusted to desired value by controller parameters regardless of the initial values. Finally, the feasibility of theoretical results are proved via simulation results.     

Further results on fixed/preassigned-time projective lag synchronization control of hybrid inertial neural networks with time delays

This article aims to study fixed-time projective lag synchronization(FXPLS) and preassigned-time projective lag synchronization(PTPLS) of hybrid inertial neural networks(HINNs) with state-switched and discontinuous activation functions(DAFs). By constructing new hybrid fixed-time control and based on theory of non-smooth analysis, we achieve novel results on FXPLS for such HINNs. Through designing novel hybrid preassigned-time control, new criteria on PTPLS of the HINNs is also taken into account. And as distinct from recent works, the FXPLS and PTPLS results are established via non-variable substitution and in a more generalized framework than common synchronization, which also has more extensive practical applications. Finally, example simulations are displayed to set forth the validity of the acquired FXPLS and PTPLS.     

Complex projective synchronization of complex-valued neural network with structure identification

This paper studies the projective synchronization of neural network in complex-valued domain. Both projective factors and neuron state variables are set as complex values in the synchronization process. In our study, unknown network structure and time-varying delays are considered. With the projective synchronization, the network structure will be identified and the problem of bounded time delays can be solved. With Lyapunov–Krasovskii stability theory and adaptive feedback scheme, controllers are designed and the complex projective synchronization is achieved. In the numerical simulation, several complex-valued neural network examples are provided showing the effectiveness of the theoretical results.     

Impulse-based coupling synchronization of multiple discrete-time memristor-based neural networks with stochastic perturbations and mixed delays

The global synchronization problem of multiple discrete-time memristor-based neural networks (DTMNNs) with stochastic perturbations and mixed delays is studied under impulse-based coupling control, where the coupling control only occurs at discrete impulse times. The impulse-based coupling control will further reduce the communication bandwidth for multiple DTMNNs to achieve coupling synchronization. We construct an array of multiple DTMNNs with stochastic perturbations and mixed delays and propose a novel impulse-based coupling control scheme. By utilizing Lyapunov–Krasovskii functional technique, schur complement technique and linear matrix inequality (LMI) method, some sufficient synchronization conditions depending on stochastic perturbations and mixed delays are established. At the end of this paper, a numerical example is given and the effectiveness of the impulse-based coupling control is illustrated by using MATLAB programming.     

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.     

State quantized sampled-data control design for complex-valued memristive neural networks

In this paper, several resultful control schemes based on data quantization are proposed for complex-valued memristive neural networks (CVMNNs). Firstly, considering the finite communication resources and the interference of failures to the system, a state quantized sampled-data controller (SQSDC) is designed for CVMNNs. Next, taking the interference of gain fluctuations into account, a non-fragile sampled-data control (SDC) law is proposed for CVMNNs in the framework of data quantification. In order to full capture more inner sampling information, a newly Lyapunov-Krasovskii function (LKF) is constructed on the basis of the proposed triple integral inequality. After that, in the framework of taking full advantage of the property of Bessel-Legendre inequality, a time-dependent discontinuous LKF (TDDLKF) is proposed for CVMNNs with SQSDC. Based on the useful LKF, several stability criteria are established. Finally, the numerical simulations are provided to substantiate the validity and less conservatism of the proposed schemes.     

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.     

Quasi-projective synchronization of discrete-time fractional-order quaternion-valued neural networks

In this article, without decomposing the quaternion-valued neural networks (QVNNs) into two complex-valued subsystems or four real-valued subsystems, quasi-projective synchronization of discrete-time fractional-order QVNNs is investigated. To this end, the sign function for quaternion number is introduced and some related properties are given. Then, two inequalities are built according to the nabla fractional difference and quaternion theory. Subsequently, a simple linear quaternion-valued controller is designed, and some synchronization conditions are given by means of our created inequalities. Finally, numerical simulations are given to prove the feasibility and correctness of the theoretical results.