@article{Wang2026, 
author = {Xinya Wang and Guici Chen and Huimin Zeng},
title = {A non-reduced-order event-triggered control to fixed/prescribed-time synchronization of fuzzy inertial memristive neural networks with distributed delays},
year = {2026},
journal = {C2 Science and Technology},
volume = {1},
number = {1},
pages = {91-98},
keywords = {memristive, fuzzy inertial neural networks, event-triggered control, fixed-time synchronization, prescribed-time synchronization},
url = {https://www.sciopen.com/article/10.26599/C2ST.2026.9700009},
doi = {10.26599/C2ST.2026.9700009},
abstract = {This paper investigates non-reduced-order event-triggered fixed-time synchronization (FxTS) and prescribed-time synchronization (PTS) for fuzzy inertial memristive neural networks with distributed delays (DFIMNNs). The original second-order model is directly analyzed to preserve the inertial structure and avoid dimension expansion. Memristive characteristics are introduced into both standard synaptic connection terms and fuzzy operation-related terms, and convex analysis is employed to handle state-dependent memristive switching, reducing the conservatism of the conventional maximum-absolute-value method. Based on this, an adjustable event-triggered controller (ETC) is designed, and sufficient criteria are derived to achieve FxTS and PTS while excluding Zeno behavior. Finally, numerical simulations and comparisons with a continuous controller verify the effectiveness of the proposed method and its advantages in reducing control updates.}
}