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

Author:

Fan Jiejie12,Ban Xiaojuan123,Yuan Manman45,Zhang Wenxing67

Affiliation:

1. Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China

2. School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China

3. Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China

4. School of Computer Science, Inner Mongolia University, Hohhot 010021, China

5. National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, Hohhot 010021, China

6. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

7. School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

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.

Funder

National Natural Science Foundation of China

Inner Mongolia University high-level talent project

Inner Mongolia Autonomous Region grassland talent project

Publisher

MDPI AG

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