Exponential synchronization of reaction-diffusion neural networks via intermittent event-triggered control

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Abstract

This work investigated the exponential synchronization (ES) of reaction-diffusion neural networks (RDNNs) via a novel aperiodic intermittent event-triggered control strategy. The proposed scheme enables each network node to independently configure its event-triggered threshold, control interval, and rest interval, thereby reducing unnecessary response-state sampling/transmission and controller updates under the assumption that the drive state is locally available. By constructing a piecewise Lyapunov functional and adopting Dini derivative analysis and graph-theory techniques, sufficient conditions for exponential synchronization were established. Furthermore, the exclusion of Zeno behavior was strictly proven to guarantee the feasibility of the designed event-triggered mechanism. Numerical simulations on a 10-node directed RDNN with fully heterogeneous node-wise configurations and 200 Monte Carlo runs validated the effectiveness and advantage of the proposed algorithm in reducing response-state sampling and transmission as well as controller updates compared with event-triggered synchronous schemes.

Keywords

aperiodic intermittent control / event-triggered control / exponential synchronization / reaction-diffusion neural networks

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