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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
Published on: May 25, 2019
Exponential stability analysis for delayed neural networks with switching parameters: average dwell time approach.
Ligang Wu1, Zhiguang Feng, Wei Xing Zheng
1Space Control and Inertial Technology Research Center, Harbin Institute of Technology, China. ligangwu@hit.edu.cn
IEEE Transactions on Neural Networks
|August 24, 2010
Summary
This study analyzes the exponential stability of switched delayed neural networks using novel Lyapunov-Krasovskii functionals and average dwell time methods. The findings provide improved conditions for stability analysis in neural network systems.
Area of Science:
- Control Theory
- Computational Neuroscience
- Systems Engineering
Background:
- Switched delayed neural networks are crucial in modeling complex dynamical systems.
- Ensuring exponential stability is vital for reliable network performance.
- Existing methods often face conservatism issues with time delays.
Purpose of the Study:
- To develop sufficient conditions for exponential stability analysis of continuous-time switched delayed neural networks.
- To address both constant and time-varying delays.
- To reduce conservatism in stability analysis.
Main Methods:
- Utilizing the average dwell time approach.
- Employing piecewise Lyapunov function techniques.
- Combining a novel Lyapunov-Krasovskii functional with delay partitioning and free-weighting matrix techniques.
Main Results:
- Sufficient conditions for exponential stability are derived for switched neural networks with constant and time-varying delays.
- Explicit decay estimates are provided.
- The proposed method's conservatism is reduced through delay partitioning.
Conclusions:
- The developed conditions effectively guarantee exponential stability for switched delayed neural networks.
- The novel Lyapunov-Krasovskii functional and delay partitioning method offer a less conservative approach.
- Numerical examples validate the theoretical results and demonstrate practical applicability.
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