2011
DOI: 10.1016/j.cnsns.2010.05.025
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Synchronization of chaotic delayed neural networks with impulsive and stochastic perturbations

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Cited by 54 publications
(15 citation statements)
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“…The results in [26] suggested that one neural network could be stabilized or destabilized by certain stochastic inputs. It implies that the stability analysis of stochastic neural networks has primary significance in the design and applications of neural networks, such as [13][14][15][16][17][18][19][20][21][22][23][24][25][26], but only few works have been done on the th moment exponentially stable for stochastic cellular neural networks [22][23][24][25].…”
Section: Introductionmentioning
confidence: 99%
“…The results in [26] suggested that one neural network could be stabilized or destabilized by certain stochastic inputs. It implies that the stability analysis of stochastic neural networks has primary significance in the design and applications of neural networks, such as [13][14][15][16][17][18][19][20][21][22][23][24][25][26], but only few works have been done on the th moment exponentially stable for stochastic cellular neural networks [22][23][24][25].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, synchronization of impulsive complex dynamical networks has been investigated by many researchers. For instance, (Li, and Rakkiyappan, (2013) ;Li, and Fu, (2011)), studied the Synchronization of chaotic delayed neural networks with impulsive controller. Recently, Zheng, (2015), investigated the problem of impulsive complex projective synchronization for drive-response complex-variable dynamical networks with complex coupling, and the dynamical networks with and without delay complex-variable system nodes.…”
Section: Introductionmentioning
confidence: 99%
“…China extensive applications in secure communication, parallel recognition, image processing, and other engineering areas [3,26,40,47]. A great deal of effective approaches such as drive-response method [12,21,42], adaptive control method [24,31,33], impulsive control method [15,46], sliding mode control method [8,9,13] have been proposed to synchronize chaotic systems. Recently, it has been found that neural networks can exhibit some complicated dynamics such as periodic oscillations and even chaotic attractors if time delays and parameters of networks are chosen appropriately [6,22].…”
Section: Introductionmentioning
confidence: 99%