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Updated: Nov 9, 2025
05:19
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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Synchronization of Chaotic Neural Networks: Average-Delay Impulsive Control.
Summary
This study introduces a novel approach to control chaotic neural networks using delayed impulsive control. The findings demonstrate that time delays can enhance chaos synchronization, offering new insights into network stability.
Area of Science:
- Neuroscience
- Control Theory
- Dynamical Systems
Background:
- Chaotic neural networks exhibit complex dynamics.
- Impulsive control is used to stabilize these networks.
- Existing methods often struggle with flexible delay bounds in control inputs.
Purpose of the Study:
- To investigate delayed impulsive control for chaotic neural network synchronization.
- To address the challenge of flexible delays in impulsive control inputs.
- To develop a relaxed condition for chaos synchronization.
Main Methods:
- Utilizing the concept of average impulsive delay (AID).
- Employing average impulsive interval (AII) and AID methods.
- Establishing a Lyapunov-based relaxed condition.
- Designing average-delay impulsive control using linear matrix inequality (LMI).
Main Results:
- A relaxed condition for chaotic neural network synchronization is established.
- Time delays in impulsive control inputs can positively contribute to synchronization.
- A method for designing average-delay impulsive control satisfying AID conditions is presented.
Conclusions:
- The proposed Lyapunov-based approach effectively synchronizes chaotic neural networks.
- The flexibility in delay bounds, addressed by AID, enhances control strategies.
- The study validates the synchronizing effect of time delays in impulsive control.
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