Synchronization of Reaction-Diffusion Complex-Valued Neural Networks via Event-Triggered Sampling Iterative Learning Control
This paper considers the synchronization of reaction-diffusion complex-valued neural networks (RDCVNNs) based on event-triggered sampling iterative learning control (ET-SILC). An event-triggered control (ETC) method is employed to reduce the control cost. A protocol of complete synchronization is proposed, which combines the advantage of both event-triggered control and sampling iterative learning control. The sufficient condition for synchronization is established using the contraction mapping principle. The numerical simulation example is utilized to confirm the effectiveness of the analysis.
Authors
- Xu Yang (ORCID: https://orcid.org/0000-0003-0553-4581)
- Yehui Liu (ORCID: https://orcid.org/0009-0000-2013-1700)
- Yanli Gan
- Xisheng Dai (ORCID: https://orcid.org/0000-0002-1600-8176)
Institutions
- Guangxi University of Science and Technology (CN)
- Liuzhou Maternal and Child Health Hospital (CN)
Publication Details
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1109/csis-iac63491.2024.10919384
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China