Finite-time passivity of neutral-type complex-valued neural networks with time-varying delays
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View abstract on PubMed
Summary
This summary is machine-generated.This study addresses finite-time passivity in complex-valued neural networks with delays. New criteria ensure finite-time boundedness and passivity, validated by numerical examples.
Area Of Science
- Complex-valued neural networks
- Control theory
- Nonlinear systems
Background
- Neural networks are crucial in AI, but their stability with delays is complex.
- Passivity is key for system stability and energy dissipation analysis.
- Neutral-type systems with time-varying delays present unique analytical challenges.
Purpose Of The Study
- Investigate finite-time passivity for neutral-type complex-valued neural networks.
- Develop sufficient conditions for finite-time boundedness (FTB) and finite-time passivity (FTP).
- Analyze the impact of time-varying delays on network stability and passivity.
Main Methods
- Lyapunov functional approach for stability analysis.
- Wirtinger-type inequality techniques to handle delays.
- Linear matrix inequalities (LMIs) for deriving sufficient conditions.
Main Results
- New sufficient conditions for finite-time boundedness (FTB) were established.
- Novel criteria for finite-time passivity (FTP) were derived.
- The proposed methods effectively analyze the stability of the specified neural network model.
Conclusions
- The derived conditions guarantee finite-time passivity and boundedness for the neural network model.
- The Lyapunov functional and LMI approach provide a robust framework for analyzing such systems.
- Numerical simulations confirm the theoretical findings and the validity of the proposed criteria.
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