Neural Processing Letters· 2026Q2
Exponential Synchronization in Memristive Neural Networks with Distributed Delays: Theoretical and Practical Analysis of Two Control Strategies
- 0citations
- Q2SCImago
- 2026year
Short summary
Two novel control strategies, time-delay feedback and event-triggered control (ETC), successfully achieve exponential synchronization in memristive neural networks with distributed delays, with ETC proven to avoid Zeno phenomenon.
AI-generated from the title and abstract; the full text is not read.
Key points
- Two control strategies (time-delay feedback and ETC) are proposed for exponential synchronization in delayed MNNs.
- Linear matrix inequality (LMI) and Lyapunov stability theory are used for controller design and analysis.
- The event-triggered controller (ETC) is theoretically proven to avoid the Zeno phenomenon.
- Numerical simulations validate the effectiveness of both control strategies in achieving synchronization.
AI-generated from the title and abstract; the full text is not read.
Abstract
This study delves into the complex problem of achieving exponential synchronization in memristive neural networks (MNNs) characterized by distributed latency. By utilizing the innovative concept of driving response systems, combined with rigorous linear matrix inequality (LMI) methods and basic Lyapunov stability theorem, we explored two different control strategies: time-delay feedback controller and event triggered controller (ETC). Both controllers have been carefully designed to facilitate exponential synchronization within the MNNs framework. Our method not only demonstrates the theoretical elegance of these control strategies, but also emphasizes their practical feasibility, and rigorously proves that Zeno phenomenon will not occur under ETC, proving the rationality of the ETC. Then, through detailed numerical simulations, we validated the correctness and effectiveness of our findings. The results indicate that both controllers are capable of achieving the required exponential synchronization, highlighting their potential in real-world applications.
The authors' abstract, as published at the source. Neural Processing Letters, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science