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Global Asymptotic Stability for Delayed Neural Networks Using an Integral Inequality Based on Nonorthogonal Polynomials.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Sep2018, Vol. 29 Issue 9, p4487-4493, 7p
- Publication Year :
- 2018
-
Abstract
- This brief is concerned with global asymptotic stability of a neural network with a time-varying delay. First, by introducing an auxiliary vector with some nonorthogonal polynomials, a slack-matrix-based integral inequality is established, which includes some existing one as its special case. Second, a novel Lyapunov–Krasovskii functional is constructed to suit for the use of the obtained integral inequality. As a result, a less conservative stability criterion is derived, whose effectiveness is finally demonstrated through two well-used numerical examples. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL neural networks
TIME-varying systems
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 29
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 131486934
- Full Text :
- https://doi.org/10.1109/TNNLS.2017.2750708