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.     

Generalized lag synchronization of multiple weighted complex networks with and without time delay

The generalized lag synchronization of multiple weighted complex dynamical networks with fixed and adaptive couplings is investigated in this paper, respectively. By designing appropriate controller, several synchronization criteria are presented for multiple weighted complex dynamical networks with and without time delay based on the selected Lyapunov functional and inequality techniques. Moreover, an adaptive scheme to update the coupling weights is also developed for ensuring the generalized lag synchronization of multiple weighted complex dynamical networks with and without time delay. Finally, two numerical examples are provided in order to validate effectiveness of the proposed generalized lag synchronization criteria.     

Impulsive effects on weak projective synchronization of parameter mismatched stochastic memristive neural networks

This paper analyses the weak projective synchronization (WPS) of the parameter mismatched memristive neural networks (MNNs) with stochastic disturbance and time delays via impulsive control. Complete synchronization cannot achieve because of the projective factor and mismatched parameters. Therefore, the WPS of practical MNNs under impulsive control strategy is studied. The augmented systems are built to utilize more information of the system and reduce the constraint conditions. Meanwhile, two types of comparison principles are used owing to the impulsive controller with and without time delays. Then, sufficient criteria for the exponential convergence of systems are obtained under the positive and negative effects of impulses. Finally, the validity of the theoretical results is verified by simulations of different conditions.     

Finite-time inter-layer projective synchronization of Caputo fractional-order two-layer networks by sliding mode control

Finite-time inter-layer projective synchronization (FIPS) of Caputo fractional-order two-layer networks (FTN) based on sliding mode control (SMC) technique is investigated in this article. Firstly, in order to realize the FIPS of FTN, a fractional-order integral sliding mode surface (SMS) is established. Then, through the theory of SMC, we design a sliding mode controller (SMCr) to ensure the appearance of sliding mode motion. According to the fractional Lyapunov direct method, the trajectories of the system are driven to the proposed SMS, and some novel sufficient conditions for FIPS of FTN are derived. Furthermore, as two special cases of FIPS, finite-time inter-layer synchronization and finite-time inter-layer anti-synchronization for the FTN are studied. Finally, this paper takes the fractional-order chaotic Lü’s system and the fractional-order chaotic Chen’s system as the isolated node of the first layer system and the second layer system, respectively. And the numerical simulations are given to demonstrate the feasibility and validity of the proposed theoretical results.     

Adaptive synchronization of complex networks with general distributed update laws for coupling weights

This paper discusses adaptive synchronization control for complex networks interacted in an undirected weighted graph, and aims to provide a novel and general approach for the design of distributed update laws for adaptively adjusting coupling weights. The proposed updating laws are very general in the sense that they encompass most weight update laws reported in the literature as special cases, and also provide new insights in the analysis of network system evolution and graph weight convergence. We show a rigorous proof for the synchronization stability of the overall complex network to a synchronized state, and demonstrate the convergence of adaptive weights for each edge to some bounded constants. A detailed comparison with available results is provided to elaborate the new features and advantages of the proposed adaptive strategies as compared with conventional adaptive laws. The effectiveness of the proposed approach is also validated by several typical simulations.     

Optimal synchronization controller design for complex dynamical networks with unknown system dynamics

In this paper, the optimal synchronization controller design problem for complex dynamical networks with unknown system internal dynamics is studied. A necessary and sufficient condition on the existence of the optimal control minimizing a quadratic performance index is given. The optimal control law consists of a feedback control and a compensated feedforward control, and the feedback control gain can be obtained by solving the well-known Algebraic Riccati Equation (ARE). Especially, in the presence of unknown system dynamics, a novel adaptive iterative algorithm using the information of system states and inputs is proposed to solve the ARE to get the optimal feedback control gain. Finally, a simulation example shows the effectiveness of the theoretical 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.     

Generalized cluster synchronization of Boolean control networks with delays in both the states and the inputs

In this paper, we study the generalized cluster synchronization problem for the Boolean control networks (BCNs) with delays in both the states and the inputs. First, by using the method of semi-tensor product of matrices, the original network is transformed into an equivalent extended system. Next, based on the updated iterative equation of the system, two types of generalized cluster synchronization are investigated: 1) generalized internal cluster synchronization within the BCN, and 2) generalized cluster synchronization between the BCN and the target reference network. Some necessary and sufficient conditions are proposed guaranteeing the realization of the generalized cluster synchronization. What is more, the gain matrices of the state-feedback controllers are explicitly designed. Numerical simulations are also given to illustrate effectiveness of the theoretical results obtained.     

Lag projective synchronization of fractional-order delayed chaotic systems

This paper considers the lag projective synchronization of fractional-order delayed chaotic systems. The lag projective synchronization is achieved through the use of comparison principle of linear fractional equation at the presence of time delay. Some sufficient conditions are obtained via a suitable controller. The results show that the slave system can synchronize the past state of the driver up to a scaling factor. Finally, two different structural fractional order delayed chaotic systems are considered in order to examine the effectiveness of the lag projective synchronization. Feasibility of the proposed method is validated through numerical simulations.     

Robust function projective synchronization of two different chaotic systems with unknown parameters

This paper deals with the function projective synchronization problem of two different chaotic systems with unknown and perturbed parameters. The parameter perturbations are assumed to appear in both drive and response systems, which perturbed about the nominal parameter values. A new robust function projective synchronization method is proposed, which is able to overcome random uncertainties of all model parameters. Corresponding numerical simulations are performed to verify and illustrate the analytical results.     

Anti-disturbance synchronization of fuzzy genetic regulatory networks with reaction-diffusion

This paper intends to focus on the anti-disturbance synchronization issue for genetic regulatory networks subject to reaction-diffusion terms based on the Takagi-Sugeno fuzzy model. In view of the fact that disturbances are widespread in actual control engineering, the stability of the aforementioned systems would be affected, therefore, ensuring the stability of closed-loop genetic regulatory networks is the main goal of this paper. The unknown disturbances are supposed to be generated by an exogenous system, which can be estimated by developing disturbance observers. Furthermore, integrating the disturbance observers with fuzzy rule-based conventional control laws, a new anti-disturbance control strategy is proposed to reject the disturbances and guarantee the desired dynamic performances. Then, by constructing a proper Lyapunov function and using advanced decoupling techniques, some sufficient conditions in the form of linear matrix inequalities, to guarantee the asymptotic stability of the error system, are obtained. Finally, an illustrated example is presented to demonstrate the effectiveness and superiority of the proposed method.     

Asynchronous synchronization of complex networks with switched adjacent matrices

This note analyzes the synchronization problem for a class of complex networks. The considered network topology, symbolized by adjacent matrices, is assumed to be time-varying and subjected to a Markov process. When designing controllers to synchronize the network, we consider the asynchronous phenomenon occurred between the mode of the adjacent matrices and that of the controllers. A properly defined stochastic process, which is related to the Markov process by a conditional probability is then introduced to indicate the controller mode. Base on this connection, a sufficient condition is derived by using Lyapunov–Krasoviskii theorem to ensure the global synchronization of the considered complex network with a predefined noise attenuation level. The design scheme is presented by solving a series of LMIs. A numerical example is finally provided to verify the effectiveness of the proposed method.     

Propagation of projective synchronization in a series connection of chaotic systems

Projective synchronization is a type of chaos synchronization where the response system states are scaled replicas of the drive system states. This paper deals with the propagation of projective synchronization in a series connection of N chaotic discrete-time drive systems and N response systems. By exploiting an observer-based approach, the paper demonstrates that dead-beat projective synchronization (i.e., exact synchronization in finite time for any scaling factor) is achieved between the nth drive and nth response systems. In particular, it is shown that projective synchronization starts from the innermost (Nth) drive-response system pair and propagates toward the outermost (first) drive-response system pair. Only a single scalar synchronizing signal connects the cascaded drive and response systems. Finally, an example illustrates the propagation of different types of chaos synchronization in a series connection consisting of a Gingerbreadman map, a third order hyperchaotic Henon map and a Lozi map.     

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.     

Hybrid modified function projective synchronization of two different dimensional complex nonlinear systems with parameters identification

In this article, a novel synchronization scheme is proposed to achieve hybrid modified function projective synchronization (HMFPS) in two different dimensional complex nonlinear systems with fully unknown parameters. In the complex space, the response system are asymptotically synchronized up to the different order’s drive system by the state transformation with a scaling function matrix, and all of unknown parameters in both drive and response systems are achieved to be identified. Based on the Lyapunov stability theory, an adaptive controller and updated laws of parameters are developed. Respectively on the ways of increased order and reduced order, the corresponding numerical simulations demonstrate the effectiveness and feasibility of the proposed scheme.     

Event-triggered synchronization control of complex networks with adaptive coupling strength

The event-triggered synchronization control problem is concerned for a class of complex networks with nonlinearly coupling function and adaptive coupling strength. Given a state-based event-trigger mechanism and the threshold, an event-triggered control method is introduced to make complex networks achieve exponential synchronization. By combining the Lyapunov stability theory and the knowledge of graph theory, a sufficient condition is established such that complex networks can achieve exponential synchronization. Then, the feasibility of the event-triggered control is analyzed. Moreover, the second-order Kuramoto oscillators is taken into account. And the event-triggered control strategy is used to make the oscillators achieve exponential synchronization. Meanwhile, two simulation results about the second-order Kuramoto oscillators are given to show the effectiveness of results.     

Pinning synchronization and adaptive synchronization of complex-valued inertial neural networks with time-varying delays in fixed-time interval

This article concentrates on pinning synchronization and adaptive synchronization problems of complex-valued inertial neural networks with time-varying delays in fixed-time interval. First, regarding complex-valued inertial neural networks model as an entirety instead of reducing this system to first-order differential equation, separating the real and imaginary parts of this system into an equivalent real-valued one, and establishing a novel Lyapunov function, the fixed-time stability for the closed-loop error system is guaranteed via partial nodes controlled directly by a new pinning controller which involves the state derivatives and other proper terms. Then, from the point of saving cost and avoiding resources waste, a new pinning adaptive controller is further developed and sufficient condition ensuring the adaptive fixed-time stability for the closed-loop error system is also derived. In the end, the effectiveness of these results is verified by a numerical example.