Robust synchronization of discontinuous Cohen–Grossberg neural networks: Pinning control approach

In this issue, the robust synchronization for a class of uncertain Cohen–Grossberg neural networks is studied, in which neuron activations are modelled by discontinuous functions(or piecewise continuous functions). Pinning state-feedback and adaptive controllers are designed to achieve global robust exponential synchronization and global robust asymptotical synchronization of drive-response-based discontinuous Cohen–Grossberg neural networks. By applying the theory of non-smooth analysis theory and the method of generalized Lyapunov functional, some criteria are given to show that the coupled discontinuous Cohen–Grossberg neural networks with parameter uncertainties can realized global robust synchronization. Some examples and numerical simulations are also shown to verify the validity of the proposed results.     

Finite-time synchronization of coupled Cohen–Grossberg neural networks with and without coupling delays

This paper analyzes synchronization in finite time for two types of coupled delayed Cohen–Grossberg neural networks (CDCGNNs). In the first type, linearly coupled Cohen–Grossberg neural networks with and without coupling delays are considered, respectively. In the second type, nonlinearly coupled Cohen–Grossberg neural networks both with and without coupling delays are discussed. By designing suitable controllers and using some inequality techniques, several criteria ensuring finite-time synchronization of the CDCGNNs with linear coupling and nonlinear coupling are derived, respectively. Moreover, the settling times of synchronization in finite time for the considered networks are also predicted. In the end, the availability for the acquired finite-time synchronization conditions is confirmed by two selected numerical examples.     

A survey on global pinning synchronization of complex networks

In practice, it is almost impossible to directly add a controller on each node in a complex dynamical network due to the high control cost and the difficulty of practical implementation, especially for large-scale networks. In order to address this issue, a pinning control strategy is introduced as a feasible alternative. The objective of this paper is first to recall some recent advancements in global pinning synchronization of complex networks with general communication topologies. A systematic review is presented thoroughly from the following aspects, including modeling, network topologies, control methodologies, theoretical analysis methods, and pinned node selection and localization schemes (pinning strategies). Fully distributed adaptive laws are proposed subsequently for the coupling strength as well as pinning control gains, and sufficient conditions are obtained to synchronize and pin a general complex network to a preassigned trajectory. Moreover, some open problems and future works in the field are also discussed.     

Finite-time synchronization of delayed dynamical networks via aperiodically intermittent control

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.     

Pinning exponential synchronization for inertial coupled neural networks via adaptive aperiodically intermittent control under directed topology

This article is mainly focused on investigating pinning exponential synchronization of inertial coupled neural networks (ICNNs) under different directed topologies. The traditional method of variable substitution is removed and replaced by non-reduced order method to investigate the dynamical behavior of second-order coupled system. Additionally, by constructing Lyapunov-Krasovskii functional and utilizing matrix decomposition theory as well as M-matrix theory, an adaptive aperiodically intermittent controller is introduced to derive several improved sufficient criteria based on linear matrix inequalities (LMIs). Finally, some examples with numerical simulation are exhibited to confirm the availability of the theoretical results.     

Cluster synchronization for directed coupled inertial reaction-diffusion neural networks with nonidentical nodes via non-reduced order method

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.     

Synchronization of dynamical networks with nonlinearly coupling function under hybrid pinning impulsive controllers

In this paper, the globally exponential synchronization problem of dynamical networks with nonlinearly coupling function is considered. Hybrid pinning control strategies are established to force the states of the network to follow some objective trajectory. The impulsive pinning controllers are used to control a fringe of nodes at the impulsive instants, while during the impulsive instants, pinning state-feedback controllers are designed to achieve the control objective. Finally, the validity of the developed techniques is evidenced by an illustrative example.     

Synchronization of multiplex networks with stochastic perturbations via pinning adaptive control

This paper investigates the synchronization problems for the multiplex networks with both inter-layer and intra-layer couplings subject to the stochastic perturbations. In particular, the topologies of all layers are not the same, so the model can represent a class of multiplex networks. To synchronize the multiplex networks onto the trajectory of a virtual leader, a pinning adaptive protocol is proposed and some pinning criteria are derived for guaranteeing complete synchronization. Furthermore, when the results are extended to the systems with time delays, the pinning adaptive strategy is still proved to be effective. Finally, a two-layer network and a three-layer network are selected for numerical simulations to illustrate the theoretical results.     

Fixed-time pinning synchronization for delayed complex networks under completely intermittent control

The present study investigates the fixed-time synchronization issue for delayed complex networks under intermittent pinning control. Different from some existing semi-intermittent controllers for finite/fixed-time synchronization, our pinning controller is designed in a complete intermittent way. In order to address the encountered theoretical analysis difficulties, a new differential inequality lemma is developed, which is suitable for the fixed-time synchronization studies under periodic or aperiodic complete intermittent control. Then, by using Lyapunov theory and pinning control approach, sufficient conditions are proposed which can guarantee the aperiodically completely intermittent-controlled delayed complex networks realizing fixed-time pinning synchronization. Moreover, the settling time is explicitly estimated, which is irrelevant to the initial values of our network systems. Additionally, as a special case, the scenario of periodic complete intermittent control is also discussed. At last, some simulation examples are utilized to confirm our theoretical outcomes.     

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.     

Pinning and impulsive synchronization control of complex dynamical networks with non-derivative and derivative coupling

In this paper, the problem of pinning and impulsive synchronization between two complex dynamical networks with non-derivative and derivative coupling is investigated. A hybrid controller, which contains a pinning controller and a pinning impulsive controller, is proposed simultaneously. Based on the Lyapunov stability theory and mathematical analysis technique, some novel criteria of synchronization are derived, which can guarantee that the response network asymptotically synchronizes to the drive network by combining pinning control and pinning impulsive control. Moreover, the restrictions about non-derivative coupling matrix, impulsive intervals and the number of pinned nodes are removed. Numerical examples are presented finally to illustrate the effectiveness of the theoretical results.     

Quasi-synchronization of coupled neural networks with reaction-diffusion terms driven by fractional brownian motion

This paper investigates the quasi-synchronization of reaction-diffusion neural networks with hybrid coupling and parameter mismatches via sampled-data control technology. First, the models of neural networks with switching parameter and fraction Brownian motion are given. As a result of parameter mismatches, synchronization is normally not possible to realize directly, then the improved Halanay’s inequality is introduced, which is an important lemma to prove that the considered networks realize quasi-synchronization. Furthermore, based on stochastic theory, Lyapunov function method and inequality techniques, some sufficient conditions are derived to guarantee the quasi-synchronization of hybrid coupled neural networks with reaction-diffusion terms driven by fractional Brownian motion. Finally, two simulation examples are given to prove the efficiency of the developed criteria.     

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.     

Synchronization of linear dynamical networks under stochastic impulsive coupling protocols

This paper investigates a stochastic impulsive coupling protocol for synchronization of linear dynamical networks based on discrete-time sampled-data. The convergence of the networks under the proposed protocol is discussed, and some sufficient conditions are showed to guarantee almost sure exponential synchronization. Moreover, this coupling protocol with a pinning control scheme is developed to lead the state of all nodes to almost sure exponentially converge to a virtual synchronization target. It is shown that the almost sure exponential synchronization can be achieved by only interacting based on the stochastic feedback information at discrete-time instants. Some numerical examples are finally provided to present the effectiveness of the proposed stochastic coupling protocols.     

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.     

Synchronization of coupled heterogeneous complex networks

By only designing the internal coupling, quasi synchronization of heterogeneous complex networks coupled by N nonidentical Duffing-type oscillators without any external controller is investigated in this paper. To achieve quasi synchronization, the average of states of all nodes is designed as the virtual target. Heterogeneous complex networks with two kinds of nonlinear node dynamics are analyzed firstly. Some sufficient conditions on quasi synchronization are obtained without designing any external controller. Quasi synchronization means that the states of all nonidentical nodes will keep a bounded error with the virtual target. Then the heterogeneous complex network with impulsive coupling which means the network only has coupling at some discrete impulsive instants, is further discussed. Some sufficient conditions on heterogeneous complex network with impulsive coupling are derived. Based on these results, heterogeneous complex network can still reach quasi synchronization even if its nodes are only coupled at discrete impulsive instants. Finally, two examples are provided to verify the theoretical results.     

Global synchronization in nonlinearly coupled delayed memristor-based neural networks with excitatory and inhibitory connections

This investigation establishes the global synchronization of an array of coupled memristor-based neural networks with delays. The coupled networks that are considered can incorporate both the internal delay in each individual network and the transmission delay across different networks. The coupling scheme, which consists of a nonlinear term and a sign term, is rather general. In particular, it can be asymmetric, and admits the coexistence of excitatory and inhibitory connections. Based on an iterative approach, the problem of synchronization is transformed into solving a corresponding linear system of algebraic equations. Subsequently, the respective synchronization criteria, which depend on whether the transmission delay exists, are derived respectively. Three examples are given to illustrate the effectiveness of the theories presented in this paper. The synchronization of the systems in two examples cannot be handled by existing techniques.     

Mean-square pinning control of fractional stochastic discrete-time complex networks

This paper considers the mean-square pinning control problem of fractional stochastic discrete-time complex networks. First, a new fractional stochastic discrete-time complex networks model with stochastic noise is established. Then, some pinning controllers and sufficient conditions are developed for the complex networks. By adopting Lyapunov energy function theory and matrix analysis theory, it proved that the synchronization of the fractional stochastic discrete-time complex networks can be achieved in a mean-square sense via pinning control. In addition, these results are extended to solve the synchronization problem of general fractional discrete-time complex networks without noise. Finally, several numerical examples are given to verify the correctness of the obtained theoretical 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.     

Pinning synchronization of fractional-order memristor-based neural networks with multiple time-varying delays via static or dynamic coupling

This paper investigates global asymptotical synchronization between fractional-order memristor-based neural networks (FMNNs) with multiple time-varying delays (MTDs) by pinning control. Two classes of coupling manners, static manner and dynamic manner, are introduced into the pinning controller respectively. For the case of static coupling, to make the controller exclude fraction, 1-norm Lyapunov function and fractional Halanay inequality in MTDs case are utilized for synthesis of controller and convergence analysis of synchronization error. For the case of dynamic coupling, a fractional differential inequality is proved and discussed in an elaborate way, and then global asymptotical synchronization is analyzed by means of Lyapunov-like function and the newly-proved inequality. Lastly, numerical simulations are carried out to show the practicability of the pinning controllers and the feasibility of the obtained synchronization criteria.