Exponential Synchronization of Complex Spatio-Temporal Networks with Reaction-Diffusion Terms via Intermittent Pinning Adaptive Control

In this paper, the synchronization problem of complex spatio-temporal networks (CSNs) with reaction-diffusion terms is studied. By using Lyapunov functional method, differential inequality method and adaptive technology, the exponential synchronization of CSNs is realized by introducing intermittent pinning adaptive control, and the sufficient conditions for network synchronization are proposed. Finally, the feasibility of the theoretical results is verified by an illustrative example. This method not only reduces the conservatism of the control gain, but also saves the production cost. These advantages make this method have broad application prospects in the actual production process.

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Published
2024-10-18
DOI
https://doi.org/10.1109/ntci64025.2024.10776566
Primary Topic
Neural Networks Stability and Synchronization
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article
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article

Exponential Synchronization of Complex Spatio-Temporal Networks with Reaction-Diffusion Terms via Intermittent Pinning Adaptive Control

Ai Jia, Meng Hui, Kang Li, Qun Ma et al.
Neural Networks Stability and Synchronization
article

Exponential Synchronization of Complex Spatio-Temporal Networks with Reaction-Diffusion Terms via Intermittent Pinning Adaptive Control

Ai Jia, Meng Hui, Kang Li, Qun Ma, Hao Zhang
article en

Abstract

In this paper, the synchronization problem of complex spatio-temporal networks (CSNs) with reaction-diffusion terms is studied. By using Lyapunov functional method, differential inequality method and adaptive technology, the exponential synchronization of CSNs is realized by introducing intermittent pinning adaptive control, and the sufficient conditions for network synchronization are proposed. Finally, the feasibility of the theoretical results is verified by an illustrative example. This method not only reduces the conservatism of the control gain, but also saves the production cost. These advantages make this method have broad application prospects in the actual production process.

Chang'an University (CN)
Openalex Percentile: Top 74%
Neural Networks Stability and Synchronization
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