Novel Robust Stability Criteria for Uncertain Stochastic Neural Networks with Time-Varying Delay
This paper considers the robust stability analysis problem for a class of uncertain stochastic neural net- works with time-varying delay. Based on the Lyapunov functional method, and by resorting to the new technique for estimating the upper bound of the stochastic derivative of Lyapunov functionals...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2009-03-01
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| Series: | International Journal of Computational Intelligence Systems |
| Online Access: | https://www.atlantis-press.com/article/1815.pdf |
| Summary: | This paper considers the robust stability analysis problem for a class of uncertain stochastic neural net- works with time-varying delay. Based on the Lyapunov functional method, and by resorting to the new technique for estimating the upper bound of the stochastic derivative of Lyapunov functionals, the novel asymptotic stability criteria are obatined in terms of Linear matrix inequalities (LMIs). Two numerical examples are presented to show the effectiveness and the less conservativeness of the proposed method. Keywords: robust stability, stochastic neural networks, time-varying delay, linear matrix inequalities. |
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| ISSN: | 1875-6883 |