Stability analysis for discrete-time neural networks with time-varying delays and stochastic parameter uncertainties
2015 ◽
Vol 93
(4)
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pp. 398-408
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Keyword(s):
This paper proposes new delay-dependent stability criteria for discrete-time neural networks with interval time-varying delays and probabilistic occurring parameter uncertainties. It is assumed that parameter uncertainties are changed with the environment, explored using random situations, and its stochastic information is included in the proposed method. By constructing a suitable Lyapunov–Krasovskii functional, new delay-dependent stability criteria for the concerned systems are established in terms of linear matrix inequalities, which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed method.
2012 ◽
Vol 2012
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pp. 1-14
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2018 ◽
Vol 2018
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pp. 1-15
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Keyword(s):
Keyword(s):
2017 ◽
Vol 40
(9)
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pp. 2868-2880
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2014 ◽
Vol 513-517
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pp. 922-926
2011 ◽
Vol 21
(1)
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pp. 127-135
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