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Coupled Impulsive Control for Multisynchronization of Multistable Stochastic Neural Networks Under Parameter
This study addresses multisynchronization for multistable stochastic neural networks (MSNNs) with time delays and uncertainties. A novel impulsive control strategy ensures synchronization, reducing control costs and verifying effectiveness in complex network dynamics.
Area of Science:
- Neuroscience
- Control Theory
- Applied Mathematics
Background:
- Multistable stochastic neural networks (MSNNs) exhibit complex dynamics relevant to realistic neural environments.
- Time-varying delays and parameter uncertainties pose significant challenges in controlling MSNNs.
- Achieving synchronization in complex networks is crucial for understanding emergent behaviors.
Purpose of the Study:
- To investigate the multisynchronization of multistable stochastic neural networks (MSNNs) under challenging conditions.
- To develop a cost-effective control strategy for achieving synchronization in MSNN systems.
- To derive sufficient conditions for multisynchronization applicable to both fixed and switching network topologies.
Main Methods:
- Construction of an MSNN model incorporating time-varying delays and parameter uncertainties.
- Adoption of a coupled impulsive control strategy to manage network synchronization.
- Development of a Lyapunov functional and application of the average impulsive interval concept.
- Analysis under fixed and switching network topologies.
Main Results:
- Sufficient conditions for achieving multisynchronization of MSNNs were successfully derived.
- The proposed impulsive control strategy effectively reduces control costs.
- The control scheme's validity was confirmed via a numerical simulation.
Conclusions:
- The study provides a robust framework for analyzing and controlling multisynchronization in complex MSNNs.
- The developed impulsive control method offers an efficient approach for synchronization in dynamic neural networks.
- The findings contribute to the theoretical understanding and practical application of synchronization in stochastic neural systems.
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