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Improved Stability Criteria for Delayed Neural Networks Using a Quadratic Function Negative-Definiteness Approach.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Mar2022, Vol. 33 Issue 3, p1348-1354. 7p. - Publication Year :
- 2022
-
Abstract
- This brief is concerned with the stability of a neural network with a time-varying delay using the quadratic function negative-definiteness approach reported recently. A more general reciprocally convex combination inequality is taken to introduce some quadratic terms into the time derivative of a Lyapunov–Krasovskii (L–K) functional. As a result, the time derivative of the L–K functional is estimated by a novel quadratic function on the time-varying delay. Moreover, a simple way is introduced to calculate the coefficients of a quadratic function, which avoids tedious works by hand as done in some studies. The L–K functional approach is applied to derive a hierarchical type stability criterion for the delayed neural networks, which is of less conservatism in comparison with some existing results through two well-studied numerical examples. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 33
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 155696533
- Full Text :
- https://doi.org/10.1109/TNNLS.2020.3042307