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Quantization synchronization of chaotic neural networks with time delay under event-triggered strategy
Ailong Wu1,2, Yue Chen1, Zhigang Zeng2
1College of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002 China.
Cognitive Neurodynamics
|October 4, 2021
Summary
This study introduces a dynamic event-triggered strategy for synchronizing delayed chaotic neural networks, ensuring reliable control and secure communication. It establishes criteria for synchronization and quantifies synchronization errors, preventing Zeno behavior.
Area of Science:
- Control Theory
- Computational Neuroscience
- Network Synchronization
Background:
- Delayed chaotic neural networks present challenges in synchronization due to inherent complexities.
- Event-triggered control strategies offer efficiency but require careful design to avoid issues like Zeno behavior.
Purpose of the Study:
- To develop a dynamic event-triggered strategy for quantized synchronization of delayed chaotic master-slave neural networks.
- To derive theoretical criteria for quasi-synchronization and establish bounds for synchronization errors.
- To ensure the absence of Zeno behavior in the proposed control scheme.
Main Methods:
- Utilizing a generalized Halanay-type inequality to derive synchronization criteria.
- Analyzing output feedback controllers under both event-triggering and quantization effects.
- Developing criteria to exclude Zeno behavior in event-triggered controllers.
Main Results:
- A theoretical criterion for quasi-synchronization of delayed chaotic neural networks was successfully derived.
- An exact upper bound for synchronization error was obtained.
- Sufficient criteria for the existence of quantized output feedback controllers were provided.
Conclusions:
- The proposed dynamic event-triggered strategy effectively achieves quantized synchronization for delayed chaotic neural networks.
- The derived criteria ensure reliable control, quantify synchronization error, and prevent Zeno behavior.
- The method's efficiency is validated through numerical examples and secure image communication experiments.
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