Synchronization of Fractional-Order Reaction–Diffusion Neural Networks via ETILC
This paper focuses on the synchronization of fractional-order reaction–diffusion neural networks (FORDNN) under sampling event-triggered iterative learning control. A Dα-type iterative learning protocol of complete synchronization is proposed, which combines the advantages of both event-triggered control and sampling iterative learning control. Using mathematical tools, sufficient conditions for synchronization are derived. A numerical simulation example is provided to confirm the effectiveness of the analysis.
Authors
- Yehui Liu (ORCID: https://orcid.org/0009-0000-2013-1700)
- Senping Tian (ORCID: https://orcid.org/0000-0001-8359-2133)
- Xisheng Dai (ORCID: https://orcid.org/0000-0002-1600-8176)
- Yanxue Wang (ORCID: https://orcid.org/0000-0001-8739-4740)
- Jianxiang Zhang (ORCID: https://orcid.org/0000-0002-0984-2947)
Institutions
- United Aircraft (Russia) (RU)
- Guangxi University of Science and Technology (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Fractal and Fractional
- Published
- 2025-11-24
- DOI
- https://doi.org/10.3390/fractalfract9120764
- Primary Topic
- Neural Networks Stability and Synchronization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China