2012
DOI: 10.1002/mma.2531
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Robust exponential stability of discrete‐time uncertain impulsive neural networks with time‐varying delay

Yu Zhang

Abstract: The purpose of this paper is to investigate the robust exponential stability of discrete‐time uncertain impulsive neural networks with time‐varying delay. By using Lyapunov functions together with Razumikhin technique, some new robust exponential stability criteria are presented. The obtained results show that the robust stability can be retained under certain impulsive perturbations for the neural network, which has the robust stability property. The obtained results also show that impulses can robustly stabi… Show more

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Cited by 20 publications
(14 citation statements)
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References 35 publications
(103 reference statements)
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“…Recently, neural networks, including the Hopfield neural networks, the Cohen‐Grossberg (C‐G) neural networks, and the cellular neural networks, have been extensively investigated and many interesting results have been obtained. () Among which, the stability analysis of the C‐G system with stochastic and reaction‐diffusion terms is considered in Shi and Zhu, whereas Liu et al paid attention to the C‐G system with Markovian jumping() and investigated the C‐G neural networks with complex‐valued parameters. However, there are no publishing results concentrated on the C‐G neural networks with quaternions parameters so far, which constituted one of the motives of this paper.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, neural networks, including the Hopfield neural networks, the Cohen‐Grossberg (C‐G) neural networks, and the cellular neural networks, have been extensively investigated and many interesting results have been obtained. () Among which, the stability analysis of the C‐G system with stochastic and reaction‐diffusion terms is considered in Shi and Zhu, whereas Liu et al paid attention to the C‐G system with Markovian jumping() and investigated the C‐G neural networks with complex‐valued parameters. However, there are no publishing results concentrated on the C‐G neural networks with quaternions parameters so far, which constituted one of the motives of this paper.…”
Section: Introductionmentioning
confidence: 99%
“…which shows that ‰ 2 < 0 is true, where ‰ i , i D 1, 2 are the same as those in (12). Finally, employing Lemma 2.3 leads to…”
Section: T Xt// Along the Trajectories Of The T-s Fuzzy Cgnnsmentioning
confidence: 57%
“…Specifically, the larger corresponds to the smaller , where denotes the level of dissipativity. Setting D 3.9354, D 0.3, ı D 0.2 in Theorem 3.1, and by using MATLAB LMI toolbox, it can be proved that LMIs (12) are solvable, and the corresponding solutions are given as follows (due to page consideration, we neglect some feasible matrices):…”
Section: Example 41mentioning
confidence: 99%
“…The development of the impulsive dynamical systems has proceeded along two distinct lines. The dynamical systems with impulses have been widely studied in [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”
Section: Introductionmentioning
confidence: 99%