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Dynamic event-triggered synchronization control for neutral-type SMJ neural networks with additive delays under
Zou Yang1, Jun Wang1, Kaibo Shi2
1College of Electrical and Information Engineering, Southwest Minzu University, Chengdu, 610041, P.R. China.
ISA Transactions
|February 11, 2025
Summary
This study introduces double dynamic event-triggered mechanisms for synchronizing semi-Markovian jump neural networks under attacks. This approach conserves resources and reduces computational load for enhanced network performance.
Area of Science:
- Control Theory
- Networked Systems
- Computational Neuroscience
Background:
- Neural networks are crucial for complex computations but vulnerable to attacks.
- Event-triggered mechanisms (ETMs) aim to reduce communication and computational load.
- Semi-Markovian jump (SMJ) systems introduce complex dynamics with state-dependent transition probabilities.
Purpose of the Study:
- To develop a double dynamic event-triggered synchronization (DDETM) strategy for neutral-type SMJ neural networks.
- To model and mitigate the impact of synchronous attacks on network synchronization.
- To reduce the conservatism of synchronization criteria through advanced mathematical techniques.
Main Methods:
- Modeling synchronous attacks using an independent semi-Markovian jump process.
- Designing double dynamic event-triggered mechanisms (DDETMs) for communication and computation efficiency.
- Incorporating additive time-varying delays (TVDs) to model network uncertainties.
- Developing a semi-Markov dynamic event-triggered controller.
- Utilizing asymmetric Lyapunov-Krasovskii functions (LKFs) and reciprocally convex inequality (RCCI) to reduce conservatism.
Main Results:
- The proposed DDETMs effectively conserve communication resources and reduce computational burden.
- The designed controller ensures the synchronization of neutral-type SMJ neural networks under synchronous attacks.
- The use of asymmetric LKFs and RCCI significantly reduces the conservatism of the synchronization criterion.
- Simulation results validate the effectiveness of the proposed synchronization method.
Conclusions:
- The developed event-triggered synchronization strategy is effective for neutral-type SMJ neural networks under synchronous attacks.
- The proposed method offers a practical approach to enhance the robustness and efficiency of networked neural systems.
- This research contributes to the advancement of secure and efficient control strategies for complex dynamical systems.
Keywords:
Cyber-attacksEvent-triggered controlLyapunov–Krasovskii functionsNeural networksSemi-Markov jump processesMore Related Videos
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