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Intermittent Control for Quasisynchronization of Delayed Discrete-Time Neural Networks
IEEE Transactions on Cybernetics
|July 23, 2020
Summary
This study introduces a novel event-dependent intermittent control for delayed discrete-time neural networks (DNNs). The new mechanism enables aperiodic controller activation, improving quasisynchronization performance.
Area of Science:
- Control Theory
- Neural Networks
- Dynamical Systems
Background:
- Delayed discrete-time neural networks (DNNs) present challenges in control and synchronization.
- Existing intermittent control schemes often rely on predictable or periodic activation patterns.
Purpose of the Study:
- To develop an event-dependent intermittent control mechanism for delayed DNNs.
- To achieve quasisynchronization in these networks with enhanced efficiency and adaptability.
Main Methods:
- An event-dependent intermittent control mechanism based on Lyapunov functions and three non-negative real regions.
- Development of sufficient conditions using linear matrix inequalities (LMIs).
- An optimization algorithm for computing control gains and Lyapunov matrices.
Main Results:
- The proposed intermittent mechanism allows aperiodic, unpredictable controller work/rest times.
- Sufficient conditions for quasisynchronization were established.
- The controller activates only when the Lyapunov function trajectory enters a specific work region.
Conclusions:
- The designed intermittent control mechanism is feasible for delayed DNNs.
- The approach offers a fundamentally new principle for intermittent control strategies.
- The method effectively stabilizes synchronization error to a minimal convergence region.
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