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Stability Analysis for Delayed Neural Networks With an Improved General Free-Matrix-Based Integral Inequality
IEEE Transactions on Neural Networks and Learning Systems
|April 30, 2019
Summary
This study presents an improved free-matrix-based integral inequality for stability analysis of neural networks with time-varying delays. The new method reduces computational burden and conservativeness for enhanced network stability.
Area of Science:
- Control Theory
- Artificial Intelligence
- Dynamical Systems
Background:
- Stability analysis is crucial for neural networks, especially those with time-varying delays.
- Existing methods often suffer from conservatism and high computational costs.
Purpose of the Study:
- To develop a novel and efficient stability analysis method for neural networks with time-varying delays.
- To reduce the conservatism and computational complexity of stability analysis.
Main Methods:
- An improved general free-matrix-based (FMB) integral inequality is proposed.
- A new Lyapunov-Krasovskii functional is constructed.
- The FMB inequality is applied to derive a new stability condition.
Main Results:
- The proposed FMB integral inequality involves fewer free matrix variables compared to conventional approaches.
- A concrete form of the inequality is derived for any undetermined number m.
- The new stability condition demonstrates reduced conservatism and computational burden.
Conclusions:
- The proposed method offers a more competitive approach for stability analysis of neural networks with time-varying delays.
- Numerical examples validate the effectiveness and efficiency of the new technique.
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