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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Dissipativity Analysis for Neural Networks With Time-Varying Delays via a Delay-Product-Type Lyapunov Functional
IEEE Transactions on Neural Networks and Learning Systems
|April 11, 2020
Summary
This study introduces a new method for analyzing the stability and dissipativity of neural networks with time-varying delays. The findings enhance understanding of complex system dynamics and improve control strategies.
Area of Science:
- Control Theory
- Artificial Neural Networks
- Systems Analysis
Background:
- Neural networks (NNs) with time-varying delays present significant challenges in stability and dissipativity analysis.
- Existing methods often struggle to fully incorporate the complexities of these time-varying delays.
Purpose of the Study:
- To develop novel conditions for ensuring strict dissipativity in NNs with time-varying delays.
- To enhance the stability analysis of such systems.
Main Methods:
- A new augmented Lyapunov-Krasovskii functional (LKF) incorporating delay-product terms is proposed.
- Generalized free-matrix-based inequalities are utilized to estimate the LKF derivative.
- Improved delay-dependent conditions are derived.
Main Results:
- The proposed method yields improved delay-dependent conditions for strict (Q, S, R)-γ-dissipativity in NNs.
- The derived conditions are effectively applied to analyze the passivity and stability of delayed NNs.
- Numerical examples and a real-world quadruple tank process demonstrate the method's efficacy.
Conclusions:
- The developed approach offers a more comprehensive framework for analyzing dissipativity and stability in NNs with time-varying delays.
- This research contributes to more robust and reliable control system design for complex dynamic systems.
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