Observer-Based Impulsive Synchronization for Neural Networks With Uncertain Exchanging Information

Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel

This work addresses the quasi-synchronization of delay master-slave BAM neural networks. To improve the utilization of channel bandwidth, a dynamic event-triggered impulsive mechanism is employed, in which data is transmitted only when a preset event-triggered mechanism or a forced impulse interval is satisfied. In addition, to guarantee the reliability of information transmission, a reliable redundant channel for BAM neural networks is adopted, whose transmission scheduling strategy is designed on the basis of the packet dropouts rate of the main communication channels. Further, an algorithm is employed to reduce the quasi-synchronization range of the error systems and the controllers are obtained. At last, a simulation result is shown to illustrate the effectiveness of the presented strategy.

Bounded Antisynchronization of Multiple Neural Networks via Multilevel Hybrid Control

The bounded antisynchronization (AS) problem of multiple discrete-time neural networks (NNs) based on the fuzzy model is studied, in consideration of the differences in quantity and communication among different NN groups, the variabilities of dynamics, and communication topological affected by environments. To reduce the energy consumption of communication, a cluster pinning communication mechanism is proposed, and an impulsive observer is designed to estimate the state of target NN. Then, a multilevel hybrid controller based on the impulsive observer is built including the AS controller and the bounded synchronization (BS) controller. Sufficient conditions for bounded AS are obtained by analyzing the stability of the BS augmented error (BSAE) and the AS augmented error (ASAE) based on the fuzzy-based Lyapunov functional (FBLF). Finally, a numerical example and an application example are given to verify the validity of the obtained results.

H∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism

In this article, the H_∞ bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter, by constructing a novel Lyapunov function depending on both the switching signal and the internal dynamic nonnegative variable of the triggering mechanism, the exponential stability of bipartite synchronization error systems in the mean-square sense is analyzed. Finally, two simulation examples are provided to illustrate the effectiveness of the derived results.

Reliable impulsive synchronization for fuzzy neural networks with mixed controllers

This work studies the synchronization of the master-slave (MS) fuzzy neural networks (FNNs) with random actuator failure, where the state information of the master FNNs can not be obtained directly. To reduce the loads of the communication channel and the controller, the simultaneously impulsive driven strategy of the communication channel and the controller is proposed. On the basis of the received measurements of the master FNNs, the mixed controller consisting of observer based controller and the static controller is designed. The randomly occurred actuator failure is also considered. According to the Lyapunov method, the sufficient conditions are achieved to ensure the synchronization of the MS FNNs, and the controller gains are designed by using the obtained results. The validity of the derived results is illustrated by a numerical example.

Nonfragile Finite-Time Synchronization for Coupled Neural Networks With Impulsive Approach

This article addresses the problem of the average stochastic finite-time synchronization (ASFTS) for a set of coupled neural networks (NNs) with energy-bounded noises. Due to the channel capacity constraint, the impulsive approach is introduced so as to cut down the communication times among the leader NNs and the follower NNs. Then, a nonfragile controller is designed to improve the robustness of the controller with randomly occurred uncertainty. The sufficient conditions that guarantee the ASFTS of the coupled NNs and the leader NNs are achieved. The boundary of the synchronization error is also obtained by constructing the monotonic increasing functions. Finally, the controller gains are given based on the derived conditions, and their effectiveness is illustrated by a numerical example.