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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
Published on: May 25, 2019
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Stability and synchronization for complex-valued neural networks with stochastic parameters and mixed time delays
Yufei Liu1,2, Bo Shen1,2, Jie Sun1,2
1College of Information Science and Technology, Donghua University, Shanghai, 201620 China.
Cognitive Neurodynamics
|October 3, 2023
Summary
This study introduces complex-valued neural networks (CVNNs) with random parameters and time delays. It establishes conditions for stability and synchronization in these advanced neural network models.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Control Theory
Background:
- Complex-valued neural networks (CVNNs) offer enhanced capabilities for processing complex data.
- Stochastic parameters and mixed time delays are crucial for realistic CVNN modeling.
Purpose of the Study:
- To propose a class of CVNNs incorporating stochastic parameters and mixed time delays.
- To investigate the stability and synchronization of these proposed CVNNs.
Main Methods:
- Lyapunov stability theory is applied to derive conditions for asymptotic stability.
- Matrix inequalities, utilizing Kronecker products, are developed to ensure synchronization.
Main Results:
- A sufficient condition for the mean-square asymptotic stability of CVNNs is established.
- Feasible matrix inequalities guarantee the synchronization of coupled identical CVNNs.
Conclusions:
- The proposed theoretical framework effectively addresses stability and synchronization in complex CVNNs.
- Numerical examples validate the practical applicability and effectiveness of the derived results.
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