Improved Results on Finite-Time Stability Analysis of Neural Networks With Time-Varying Delays
2018 ◽
Vol 140
(10)
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Keyword(s):
This paper investigates the issue of finite time stability analysis of time-delayed neural networks by introducing a new Lyapunov functional which uses the information on the delay sufficiently and an augmented Lyapunov functional which contains some triple integral terms. Some improved delay-dependent stability criteria are derived using Jensen's inequality, reciprocally convex combination methods. Then, the finite-time stability conditions are solved by the linear matrix inequalities (LMIs). Numerical examples are finally presented to verify the effectiveness of the obtained results.
2021 ◽
Vol 19
(3)
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pp. 199
Keyword(s):
2019 ◽
Vol 51
(1)
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pp. 407-426
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Keyword(s):
Keyword(s):
2017 ◽
Vol 28
(12)
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pp. 2924-2935
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2013 ◽
Vol 2013
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pp. 1-12
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Keyword(s):