Pinning Event-Triggered Scheme for Synchronization of Delayed Uncertain Memristive Neural Networks

Jiejie Fan, Xiaojuan Ban (), Manman Yuan () and Wenxing Zhang
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Jiejie Fan: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
Xiaojuan Ban: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
Manman Yuan: School of Computer Science, Inner Mongolia University, Hohhot 010021, China
Wenxing Zhang: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Mathematics, 2024, vol. 12, issue 6, 1-28

Abstract: To reduce the communication and computation overhead of neural networks, a novel pinning event-triggered scheme (PETS) is developed in this paper, which enables pinning synchronization of uncertain coupled memristive neural networks (CMNNs) under limited resources. Time-varying delays, uncertainties, and mismatched parameters are all considered, which makes the system more interpretable. In addition, from the low energy cost point of view, an algorithm for pinned node selection is designed to further investigate the newly event-triggered function under limited communication resources. Meanwhile, based on the PETS and following the Lyapunov functional method, sufficient conditions for the pinning exponential stability of the proposed coupled error system are formulated, and the analysis of the self-triggered method shows that our method can efficiently avoid Zeno behavior under the newly determined triggered conditions, which contribute to better PETS performance. Extensive experiments demonstrate that the PETS significantly outperforms the existing schemes in terms of solution quality.

Keywords: event-triggered mechanism; memristor; Zeno behavior; synchronization; pinning control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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