A semi-looped functional for sampled-data synchronization of delayed neural networks considering communication delay
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View abstract on PubMed
Summary
This summary is machine-generated.This study presents a new method for master-slave synchronization in delayed neural networks (DNNs) using sampled-data control. The approach allows for larger sampling intervals, reducing communication and computational loads.
Area Of Science
- Control Systems Engineering
- Computational Neuroscience
- Networked Systems
Background
- Delayed Neural Networks (DNNs) are crucial in modeling complex biological and artificial systems.
- Master-slave synchronization in DNNs is essential for coordinated information processing but challenging due to delays.
- Existing synchronization methods often impose restrictive conditions, limiting practical application.
Purpose Of The Study
- To develop a novel sampled-data controller for achieving master-slave synchronization in DNNs with communication delays.
- To reduce conservatism in synchronization criteria, enabling larger sampling intervals.
- To decrease communication and computational burdens in networked DNN systems.
Main Methods
- Construction of a novel semi-looped functional to incorporate more system information and relax constraints.
- Development of two zero-value equations to coordinate system information and enhance controller design flexibility.
- Derivation of a reduced-conservatism synchronization criterion using the proposed techniques.
Main Results
- A new synchronization criterion for delayed neural networks with communication delays was successfully derived.
- The proposed criterion allows for significantly larger sampling intervals compared to existing methods.
- Effectiveness and superiority were validated through three numerical examples.
Conclusions
- The developed sampled-data synchronization controller effectively addresses master-slave synchronization in DNNs with communication delays.
- The proposed method offers a more flexible and less conservative approach to synchronization design.
- This research contributes to more efficient and practical implementations of synchronized delayed neural networks.
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