Finite-time stochastic stabilization for BAM neural networks with uncertainties

Liu, Xiaoyang; Jiang, Nan; Cao, Jinde; Wang, Shumei; Wang, Zhengxin

doi:10.1016/j.jfranklin.2013.05.027

Cited by 68 publications

(44 citation statements)

References 33 publications

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“…If Lyapunov functionV (t) ≤ −αV (t) (p = 0) when controllers are added into the network and the control rate θ = 1. Many previous results focus on this case (see [8,[24][25][26][27]). …”

Section: Remarkmentioning

confidence: 99%

“…) 2 , the error systems (4) would be finite-time synchronized via continuous control, while this trivial case have been investigated by many previous works [8,[24][25][26][27]29]. Hence, this case is not discussed in this paper.…”

Section: Fast Synchronization Criteriamentioning

confidence: 99%

“…The role of the parameters µ, k have been discussed by Ref. [26], and the convergence time determining by the parameter θ has been studied by Refs. [20][21].…”

Section: Fast Synchronization Criteriamentioning

confidence: 99%

“…This means that the trajectories of the slave system can reach to the trajectories of the master system over the infinite horizon by using intermittent control method. From a practical point of view, it is a challenging but significant that the synchronization objective is realized in a finite time [22][23][24][25][26][27][28]. In order to achieve a convergence time in a given time, finite-time intermittent control methods can work out efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Fast synchronization of complex dynamical networks with time-varying delay via periodically intermittent control

Fan

Zhu

et al. 2016

Neurocomputing

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The fast synchronization problem for a class of complex dynamical networks with time varying delay by means of periodically intermittent control is studied. Based on the finite-time stability theory and periodically intermittent control technique, some sufficient synchronization criteria are obtained to guarantee the fast synchronization. Furthermore, the essential condition for guaranteeing periodically intermittent control realized in finite time is given in this paper. Finally, two examples are illustrated to verify the proposed theoretical results.

show abstract

Section: Remarkmentioning

confidence: 99%

Section: Fast Synchronization Criteriamentioning

confidence: 99%

“…The role of the parameters µ, k have been discussed by Ref. [26], and the convergence time determining by the parameter θ has been studied by Refs. [20][21].…”

Section: Fast Synchronization Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast synchronization of complex dynamical networks with time-varying delay via periodically intermittent control

Fan

Zhu

et al. 2016

Neurocomputing

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show abstract

“…Recently, BAM neural networks as well as their various promotions have attracted more attention of many researchers like mathematicians, biologist, physicists and computer scientists (see [4,17,20,22,23,27,29,30]), because the BAM network structure can lead to better results in their potential applications in associative memory, nonlinear optimization problems and parallel computation than the regular neural network structures.…”

Section: Introductionmentioning

confidence: 99%

New criteria on exponential synchronization and existence of periodic solutions of complex BAM networks with delays

Yang¹,

Huang²

2017

J. Nonlinear Sci. Appl.

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In this paper, we study a class of time-delayed BAM neural networks with discontinuous activations. Base on the framework of differential inclusion theory and set-valued analysis, by designing discontinuous feedback controller and using some analytic methods, easily verifiable delay-independent criteria are established to guarantee the existence of periodic solution and global exponential synchronization of the drive-response system. Finally, we give a numerical example to illustrate our theoretical analysis. The obtained results are essentially new and they extend previously known results.

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Mittag‐Leffler state estimator design and synchronization analysis for fractional‐order BAM neural networks with time delays

Anbalagan

Raja

Rajchakit

et al. 2019

Adaptive Control & Signal

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Item Type Article Authors Pratap, A.; Dianavinnarasi, J.; Raja, R.; Rajchakit, G.; Cao, J.; Bagdasar, Ovidiu Citation Rajchakit, G., et al (2019) 'Mittag-Leffler state estimator design and synchronization analysis for fractional order BAM neural networks with time delays'. AbstractThis paper deals with the extended design of Mittag-Leffler state estimator and adaptive synchronization for fractional order BAM neural networks (FBNNs) with time delays. By the aid of Lyapunov direct approach and Razumikhin-type method a suitable fractional order Lyapunov functional is constructed and a new set of novel sufficient condition are derived to estimate the neuron states via available output measurements such that the ensuring estimator error system is globally Mittag-Leffler stable. Then, the adaptive feedback control rule is designed, under which the considered FBNNs can achieve Mittag-Leffler adaptive synchronization by means of some fractional order inequality techniques. Moreover, the adaptive feedback control may be utilized even when there is no ideal information from the system parameters. Finally, two numerical simulations are given to reveal the effectiveness of the theoretical consequences.

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Finite-time stochastic stabilization for BAM neural networks with uncertainties

Cited by 68 publications

References 33 publications

Fast synchronization of complex dynamical networks with time-varying delay via periodically intermittent control

Fast synchronization of complex dynamical networks with time-varying delay via periodically intermittent control

New criteria on exponential synchronization and existence of periodic solutions of complex BAM networks with delays

Mittag‐Leffler state estimator design and synchronization analysis for fractional‐order BAM neural networks with time delays

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