Exponential stability of inertial BAM neural networks with time-varying delay via periodically intermittent control

Zhang, Wei; Li, Chuandong; Huang, Tingwen; Tan, Jie

doi:10.1007/s00521-015-1838-7

Cited by 57 publications

(20 citation statements)

References 23 publications

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“…Remark With the reduced‐order method, the researchers in other works studied the inertial NNs different from the method used in those works. Note that the research object is the second‐order inertial NNs; it may be more meaningful and reasonable to design second‐order response inertial NNs with control inputs other than the reduced‐order systems.…”

Section: Asymptotic Adaptive Synchronization Resultsmentioning

confidence: 99%

“…Recently, inertial NNs with delay have been widely investigated by many authors . Ke and Miao, studied the global exponential stability of inertial Cohen‐Grossberg NNs with time delays by constructing a proper variable substitution to transform the original system to first‐order differential equation.…”

Section: Introductionmentioning

confidence: 99%

“…Ke and Miao, studied the global exponential stability of inertial Cohen‐Grossberg NNs with time delays by constructing a proper variable substitution to transform the original system to first‐order differential equation. By the same idea, the authors studied several model of INNs. Therefore, in those existing paper, a common method is transforming the original INNs into the first‐order differential equations by choosing suitable variable substitutions.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Lipschitz measure and adaptive control for stability and synchronization in delayed inertial Cohen‐Grossberg–type neural networks

Aouiti

Assali

2019

Adaptive Control & Signal

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In this paper, without transforming the original inertial neural networks into the first-order differential equation by some variable substitutions, time-varying delays are introduced into inertial Cohen-Grossberg-type networks and the existence, the uniqueness, and the asymptotic stability and synchronisation for the neural networks are investigated. Firstly, the existence of a unique equilibrium point is proved by using nonlinear Lipschitz measure method. Second, by finding a new Lyapunov-Krasovskii functional, some sufficient conditions are derived to ensure the asymptotic stability, the asymptotic synchronization, and the asymptotic adaptive synchronization. The results of this paper are new and they complete previously known results. We illustrate the effectiveness of the approach through a few examples.

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Section: Asymptotic Adaptive Synchronization Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear Lipschitz measure and adaptive control for stability and synchronization in delayed inertial Cohen‐Grossberg–type neural networks

Aouiti

Assali

2019

Adaptive Control & Signal

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“…From the literature review of inertial neural networks, the bifurcation in a single inertial neuron model is discussed in He et al (2012), Li et al (2004), Liu et al (2009) and the stability of an inertial two-neuron system in Wheeler and Schieve (1997). Furthermore, the stability analysis of Bidirectional Associative Memory (BAM) inertial neural networks has been discussed in Cao and Wan (2014), Yunkuan and Chunfang (2012), Zhang et al (2015) and Ke and Miao (2013) deals with the inertial Cohen-Grossberg type neural networks in the literature.…”

Section: Introductionmentioning

confidence: 98%

Stability and synchronization analysis of inertial memristive neural networks with time delays

et al. 2016

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This paper is concerned with the problem of stability and pinning synchronization of a class of inertial memristive neural networks with time delay. In contrast to general inertial neural networks, inertial memristive neural networks is applied to exhibit the synchronization and stability behaviors due to the physical properties of memristors and the differential inclusion theory. By choosing an appropriate variable transmission, the original system can be transformed into first order differential equations. Then, several sufficient conditions for the stability of inertial memristive neural networks by using matrix measure and Halanay inequality are derived. These obtained criteria are capable of reducing computational burden in the theoretical part. In addition, the evaluation is done on pinning synchronization for an array of linearly coupled inertial memristive neural networks, to derive the condition using matrix measure strategy. Finally, the two numerical simulations are presented to show the effectiveness of acquired theoretical results.

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“…Meanwhile, Hu and Cao (2015) investigated the pinning synchronization of coupled inertial delayed neural networks, by utilizing matrix measure strategy and Lyapunov function approach. Moreover, in Zhang and Li (2015), the exponential stability of inertial BAM neural networks with time-varying delay was arrived via periodically intermittent control.…”

Section: Introductionmentioning

confidence: 99%

Finite-time synchronization of inertial neural networks

Cui

Jiang

et al. 2017

Journal of the Association of Arab Universities for Basic and A

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In this paper, the finite-time synchronization of inertial neural networks is investigated. First, to realize synchronization of the master-slave system, continuous and discontinuous controllers are designed, respectively. By constructing Lyapunov function and using inequalities, some effective criteria are provided to realize synchronization in finite time. Furthermore, in order to achieve synchronization with a fast speed, a new switching controller is presented, and the upper bounds of the settling time of synchronization are estimated. Finally, several numerical simulations are presented to demonstrate the validity of the theoretical results and the effectiveness of the proposed method. Ó 2017 University of Bahrain. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Exponential stability of inertial BAM neural networks with time-varying delay via periodically intermittent control

Cited by 57 publications

References 23 publications

Nonlinear Lipschitz measure and adaptive control for stability and synchronization in delayed inertial Cohen‐Grossberg–type neural networks

Nonlinear Lipschitz measure and adaptive control for stability and synchronization in delayed inertial Cohen‐Grossberg–type neural networks

Stability and synchronization analysis of inertial memristive neural networks with time delays

Finite-time synchronization of inertial neural networks

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