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Event-Based Synchronization for Multiple Neural Networks With Time Delay and Switching Disconnected Topology
IEEE Transactions on Cybernetics
|January 25, 2020
Summary
This study addresses synchronization in multiple delayed neural networks (MDNNs) using an event-triggering strategy. It establishes conditions for event-based synchronization while avoiding Zeno behavior in directed switching topologies.
Area of Science:
- Control Theory
- Applied Mathematics
- Computational Neuroscience
Background:
- Synchronization is crucial for complex systems, including neural networks.
- Multiple delayed neural networks (MDNNs) present unique challenges due to inherent time lags.
- Directed switching topologies add complexity to network dynamics.
Purpose of the Study:
- To investigate event-based synchronization for MDNNs with directed switching topologies.
- To develop novel mathematical tools for analyzing delayed systems.
- To ensure the avoidance of Zeno behavior in the proposed synchronization strategy.
Main Methods:
- A generalized Halanay-type inequality for systems with delay was introduced.
- An iterative method was employed to derive synchronization conditions.
- Event-triggering rules were designed and analyzed for efficacy.
Main Results:
- Sufficient conditions for event-based quasisynchronization in MDNNs with sequentially connected topology were established.
- The designed event-triggering strategy was proven to avoid Zeno behavior.
- The methodology was extended to MDNNs with jointly connected topologies.
Conclusions:
- The proposed event-triggering strategy effectively achieves synchronization in MDNNs under complex topological conditions.
- The generalized inequality provides a valuable tool for analyzing delayed dynamical systems.
- The findings contribute to the robust control and analysis of neural network systems.
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