Synchronization of time-delay systems with impulsive delay via an average impulsive estimation approach

We investigated synchronization of dynamic systems with mixed delays and delayed impulses. Using impulsive control method and the average impulsive interval approach, several Lyapunov sufficient conditions were given for ensuring synchronization in terms of impulsive perturbation and impulsive control, respectively. The derived conditions indicated that delays in continuous dynamical systems were flexible under impulsive perturbation and were not strictly dependent on the size of impulsive delays, and they may have a potential impact on synchronization of the considered system. In addition, applying the proposed concepts of average positive impulsive estimation and average impulsive estimation, we integrated the information in impulsive delay into the rate coefficient to eliminate the limitation of having the same threshold at each impulse point, while the impulsive delay maintained the synchronization effect. This was an improvement on the previous results obtained. Finally, we provided two numerical examples to illustrate the validity of our results

Synchronization analysis of coupled fractional-order neural networks with time-varying delays

In this paper, the complete synchronization and Mittag-Leffler synchronization problems of a kind of coupled fractional-order neural networks with time-varying delays are introduced and studied. First, the sufficient conditions for a controlled system to reach complete synchronization are established by using the Kronecker product technique and Lyapunov direct method under pinning control. Here the pinning controller only needs to control part of the nodes, which can save more resources. To make the system achieve complete synchronization, only the error system is stable. Next, a new adaptive feedback controller is designed, which combines the Razumikhin-type method and Mittag-Leffler stability theory to make the controlled system realize Mittag-Leffler synchronization. The controller has time delays, and the calculation can be simplified by constructing an appropriate auxiliary function. Finally, two numerical examples are given. The simulation process shows that the conditions of the main theorems are not difficult to obtain, and the simulation results confirm the feasibility of the theorems

Fixed Point Results for Weak φ

By using a nontrivial proof method, the purpose of this paper is to obtain some fixed point results for weak φ-contractions in cone metric spaces over Banach algebras. Several examples and applications to the existence and uniqueness of a solution to two classes of equations are also given

Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

In this paper, we investigate the exponential synchronization problem of memristive neural networks (MNNs) with discrete and distributed time-varying delays under event-triggered control. An event-triggered controller with the static and dynamic event-triggering conditions is designed to improve the efficiency of resource utilization. By constructing a new Lyapunov function, some sufficient criteria are obtained to realize the exponential synchronization of considered drive-response MNNs under the designed event-triggered controller. In addition, the Zeno behavior will not occur by proving that the event-triggering interval has a positive lower bound under different event-triggering conditions. Finally, a numerical example is provided to prove the validity of our theoretical results

Fixed Point Results for Weak φ-Contractions in Cone Metric Spaces over Banach Algebras and Applications

By using a nontrivial proof method, the purpose of this paper is to obtain some fixed point results for weak φ-contractions in cone metric spaces over Banach algebras. Several examples and applications to the existence and uniqueness of a solution to two classes of equations are also given

Robustness Analysis of BAM Cellular Neural Network with Deviating Arguments of Generalized Type

By generating equivalent integral equations, we analyze the existence and uniqueness of solutions of bidirectional associative memory cellular neural network (BAMCNN) with deviating arguments firstly. Secondly, the question of robustness of stability (RoS) of BAMCNN with deviating argument is studied. Using the Gronwall inequality, we calculate the upper bounds of the interference intensities that can maintain the initial stability of system. The perturbed BAMCNN will maintain its original stability if the strength of one or more perturbations is less than the upper bounds that we calculated in this study. To demonstrate the validity of the conjectural values, a variety of numerical illustrations are provided