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This article focuses on the problem of adaptive event-triggered anti-synchronization control for bidirectional associative memory neural networks subject to energy-limited denial of service attacks. First, a novel adaptive event-triggered scheme is developed by resorting to the acknowledgment character technique, which can help conserve valuable communication resources and has better performance in resisting malicious cyber attacks compared to traditional schemes. Second, a more general attack strategy for denial of service attacks is proposed with the consideration of energy constraints, and an anti-synchronization error system is established to analyze the anti-synchronization behavior. Then, sufficient conditions are provided to guarantee the anti-synchronization of drive and response bidirectional associative memory neural networks in H ∞ sense. Next, a design approach is obtained based on the above conditions for the controller gains. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed method and its superiority over the traditional event-triggered scheme. [ABSTRACT FROM AUTHOR]

This article delves into the issue of the pinning synchronization of complex dynamical networks with Markov topologies. First, to reduce communication demands and implement easily in practice, an adaptive event-triggered protocol for complex networks is developed. Later, to reduce conservatism, the T-S fuzzy model is introduced to linearize adaptive triggering condition, and the corresponding fuzzy controller is designed. This method can be applied to a triggered condition with a bound and nonlinear adaptive law, and the better synchronization performance can be arrived with the assistance of the fuzzy controller. Second, the switched pinning strategy is designed, which further saves the energy and avoids the occurrence of communication congestion. Then, by introducing a hidden Markov chain, an asynchronous pinning strategy is further developed. With the assistance of convex optimal techniques, some sufficient criteria are presented to reach the desired synchronization. Finally, the examples on circular trail and Chua's circuits are exploited to demonstrate the feasibility of the above synchronization laws. • The T-S fuzzy model is introduced to cope with the adaptive process of AETM for the first time. • A Markov switched pinning strategy is constructed to decline communication congestion and cost. • Asynchronous phenomenon of pinning control is also considered, and the presented example is practical. [ABSTRACT FROM AUTHOR]

In this paper, an event-based asynchronous H ∞ filter design method for Markov jump systems over a finite-time interval is proposed. First, the hidden Markov model with a known conditional probability matrix is introduced to describe the asynchronous phenomena due to the failure of precisely obtaining the system mode information. Accordingly, an asynchronous event-triggered transmission strategy with a general threshold is presented to availably save the communication costs. Then, via using the mode-dependent Lyapunov function, a full-order filter regulated by the event-triggered strategy is established to track the system outputs. Based on Finsler's lemma, sufficient conditions with less conservatism are deduced such that the resulting system is stochastically finite-time bounded while satisfying prescribed H ∞ performance. Finally, a simulation example is implemented to illustrate the effectiveness of the theoretical results. • Asynchronous constraints are considered in both of the filter and event-triggered scheme. • Event-based transmission scheme is established with a general threshold. • The conservatism of the proposed method is reduced by introducing Finsler's lemma. • The asynchronous filtering problem for Markov jump systems is addressed in finite time. [ABSTRACT FROM AUTHOR]