Stability criteria for BAM neural networks with leakage delays and probabilistic time-varying delays

Lakshmanan, S.; Park, Ju H.; Lee, Tae Hee; Jung, Ho-Youl; Rakkiyappan, R.

doi:10.1016/j.amc.2013.03.070

Cited by 73 publications

(34 citation statements)

References 43 publications

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“…Furthermore, Liu [15] discussed the global exponential stability for BAM neural networks with time-varying leakage delays, which extended and improved the main results in [13,14]. In addition, Lakshmanan et al considered the stability of BAM neural networks with leakage delays and probabilistic time-varying delays via the Lyapunov-Krasovskii functional and stochastic analysis approach [16]. Li et al [17] investigated the existence, uniqueness and stability of recurrent neural networks with leakage delay under impulsive perturbations and showed that the impact of leakage delay cannot be ignored.…”

Section: Introductionmentioning

confidence: 95%

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

Yang

Lin

2015

Neural Comput & Applic

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This paper is concerned with the stabilization of bidirectional associative memory neural networks with time-varying delays in the leakage terms using sampleddata control. We apply an input delay approach to change the sampling system into a continuous time-delay system. Based on the Lyapunov theory, some stability criteria are obtained. These conditions are expressed in terms of linear matrix inequalities and can be solved via standard numerical software. Finally, one numerical example is given to demonstrate the effectiveness of the proposed results.

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Section: Introductionmentioning

confidence: 95%

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

Yang

Lin

2015

Neural Comput & Applic

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“…China. 4 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China.…”

Section: Examplesmentioning

confidence: 99%

Global asymptotic stability of piecewise homogeneous Markovian jump BAM neural networks with discrete and distributed time-varying delays

Zhong

et al. 2016

Adv Differ Equ

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In this paper, the issue of a global asymptotic stability analysis is developed for piecewise homogeneous Markovian jump BAM neural networks with mixed time delays. By establishing the Lyapunov functional, using mode-dependent discrete delay and applying the linear matrix inequality (LMI) method, a novel sufficient condition is obtained to guarantee the stability of the considered system. A numerical example is provided to demonstrate the feasibility and effectiveness of the proposed results.

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“…Then, the analysis of the delayed BAM neural networks has received considerable attention; especially, dynamical behaviors of delayed BAM neural networks have been extensively investigated. It can be noticed that most of the existing works focus on the stability analysis [3,4], synchronization problem [5], periodic oscillation [6,7], optional problem [8,9], or bifurcation [10,11]. In [3], a new robust stability was studied for a class of uncertain neutraltype neural networks with discrete and distributed delays.…”

Section: Introductionmentioning

confidence: 99%

Global exponential stabilization of delayed BAM neural networks: a matrix measure approach

Li¹

2018

Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)

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Abstract:In this paper, global exponential stabilization of bidirectional associative memory (BAM) neural networks with time delays is investigated. Based on the Lyapunov stability theory, we present several sufficient conditions for the global exponential stability of the equilibrium point of the BAM neural networks with delays via matrix measure approach. The presented results are easy to verify and simple to implement in practice. Finally, one numerical example is given to illustrate the feasibility and effectiveness of our theoretical results.

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Stability criteria for BAM neural networks with leakage delays and probabilistic time-varying delays

Cited by 73 publications

References 43 publications

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

Global asymptotic stability of piecewise homogeneous Markovian jump BAM neural networks with discrete and distributed time-varying delays

Global exponential stabilization of delayed BAM neural networks: a matrix measure approach

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