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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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Variable Gain Impulsive Synchronization for Discrete-Time Delayed Neural Networks and Its Application in Digital
IEEE Transactions on Neural Networks and Learning Systems
|October 10, 2023
Summary
This study introduces a novel double Lyapunov method for stabilizing and synchronizing discrete-time delayed neural networks (DDNNs) with input disturbances. Variable gain controllers improve disturbance rejection and allow for larger impulse intervals.
Area of Science:
- Control Theory
- Computational Neuroscience
- Network Science
Background:
- Discrete-time delayed neural networks (DDNNs) are crucial in modeling complex systems.
- Impulsive stabilization and synchronization are challenging problems, especially with input channel disturbances.
- Existing methods often suffer from conservatism and limited adaptability.
Purpose of the Study:
- To develop a new Lyapunov approach for analyzing exponential input-to-state stability (EISS) in impulsive DDNNs.
- To design effective impulsive controllers for stabilization and synchronization with enhanced disturbance rejection.
- To reduce conservatism and improve the flexibility of control strategies for DDNNs.
Main Methods:
- A novel double Lyapunov functional approach is introduced.
- A pair of time-dependent Lyapunov functionals are constructed for impulsive DDNNs.
- Design criteria are derived using linear matrix inequalities (LMIs).
Main Results:
- The proposed method reduces conservatism compared to previous techniques.
- Variable gain impulsive controllers are designed, offering greater flexibility.
- Numerical simulations demonstrate superior performance in disturbance attenuation and acceptance of larger impulse intervals.
- Effectiveness is validated through applications in digital signal and image encryption.
Conclusions:
- The double Lyapunov functional approach provides a powerful tool for analyzing and controlling impulsive DDNNs.
- Variable gain controllers enhance robustness and performance in the presence of disturbances.
- The findings have practical implications for secure communication and complex system control.
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