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Exponential stability on stochastic neural networks with discrete interval and distributed delays
Rongni Yang1, Zexu Zhang, Peng Shi
1Space Control and Inertial Technology Research Center, Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China. yangrongni621@gmail.com
IEEE Transactions on Neural Networks
|December 17, 2009
Summary
This study presents new stability criteria for stochastic neural networks with time-varying delays. The developed methods reduce conservatism in stability analysis for these complex systems.
Area of Science:
- Control Theory
- Artificial Intelligence
- Dynamical Systems
Background:
- Stochastic neural networks (SNNs) are crucial in modeling complex systems.
- Stability analysis of SNNs is challenging due to time-varying delays.
- Existing methods often yield conservative results.
Purpose of the Study:
- To develop novel, less conservative stability criteria for SNNs.
- To address SNNs with discrete interval and distributed time-varying delays.
- To formulate stability conditions using linear matrix inequalities (LMIs).
Main Methods:
- Construction of a novel Lyapunov-Krasovskii functional.
- Partitioning the lower bound of interval time-varying delays.
- Derivation of delay-dependent stability criteria.
Main Results:
- New stability criteria for SNNs with interval and distributed delays were established.
- The proposed criteria are less conservative than existing methods.
- Results are presented in the form of linear matrix inequalities (LMIs).
Conclusions:
- The developed Lyapunov-Krasovskii functional and partitioning method improve stability analysis for SNNs.
- The new criteria offer enhanced performance and reduced conservatism.
- Numerical examples validate the effectiveness of the proposed approach.
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