2009
DOI: 10.1016/j.neucom.2008.08.017
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Novel delay-dependent criteria for global robust exponential stability of delayed cellular neural networks with norm-bounded uncertainties

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Cited by 33 publications
(15 citation statements)
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“…[39]. Therefore, one can easily derive a corresponding result for the routine norm-bounded uncertainty from Theorem 3.…”
Section: Remarkmentioning
confidence: 94%
See 1 more Smart Citation
“…[39]. Therefore, one can easily derive a corresponding result for the routine norm-bounded uncertainty from Theorem 3.…”
Section: Remarkmentioning
confidence: 94%
“…For norm-bounded uncertain neural network with time-varying delay, by using Jensen integral inequality and LMI techniques, Qiu et al [13,14] achieved delay-dependent robust stability criteria; By using LMI techniques, Ou [11] obtained a delay-dependent criterion for the uniqueness and robust stability; By applying homeomorphism and LMIs techniques, Zheng et al [39] established a delay-dependent criterion for the existence, uniqueness and robust stability. For norm-bounded uncertain neural network with multiple time-varying delay, by involving the free weight method, using LMI and Jensen integral inequality techniques, Gau et al [4] proposed delay-dependent criteria for the uniqueness and robust stability; By using LMIs techniques, Wang et al [23] presented a delay-dependent robust stability criterion.…”
Section: Introductionmentioning
confidence: 99%
“…Lemma 3 (see Zhang et al [26]; Zheng et al [27]) Assuming that function g j (s) is defined such that 0 g j (s)/s j ( j >0), then the following inequality holds…”
Section: Lemma 1 (See Sanchez and Perezmentioning
confidence: 99%
“…Thus, the stability of equilibrium points is a prerequisite when CNNs are applied to problems of image processing, signal processing, or nonlinear algebraic equations. Many delay-independent and delay-dependent stability criteria for CNNs have been proposed over the past years, mainly based on Razumikhin techniques, the Lyapunov-Krasovskii functionals and linear matrix inequalities (LMIs) formulation [6][7][8][9][10][11]. However, the stability of many practical neural networks cannot always be guaranteed by these techniques.…”
Section: Introductionmentioning
confidence: 99%