2017
DOI: 10.1016/j.neucom.2016.09.017
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Finite-time stability of genetic regulatory networks with impulsive effects

Abstract: We study the finite-time stability of genetic regulatory networks with impulsive effects. Using the method of Lyapunov function, sufficient conditions of the finite-stability, in terms of linear matrix inequalities, are established. A numerical example is provided to further illustrate the significance of our results.

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Cited by 51 publications
(33 citation statements)
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“…Recently, many kinds of finite-time issues have attracted particular research interests, and there have been some results on finite-time stabilization and synchronization [40][41][42][43][44][45][46][47]. However, to the best of the authors knowledge, there have been very few results on the finite-time stability problem for delayed GRNs with time delays [48,49], and the purpose of this study is therefore to shorten such a gap.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, many kinds of finite-time issues have attracted particular research interests, and there have been some results on finite-time stabilization and synchronization [40][41][42][43][44][45][46][47]. However, to the best of the authors knowledge, there have been very few results on the finite-time stability problem for delayed GRNs with time delays [48,49], and the purpose of this study is therefore to shorten such a gap.…”
Section: Introductionmentioning
confidence: 99%
“…As is well known to us all, impulsive effect is a common phenomenon, and some recent novel works have been developed for impulsive systems [22], [45]. Especially, considering the sudden changes in gene regulation, the impulsive effect on genetic regulatory networks attracts many scholars' interest [40], [56]. It is noted that the ASSR method was also applied to Boolean networks with impulsive effects [5], [49]- [51].…”
Section: Introductionmentioning
confidence: 99%
“…Mutations of dynamic systems are widespread and often result in unexpected changes in various realistic fields, including neural networks [36, 37], cooperative models [38], and genetic regulatory networks [39]. For the convenience of research, these mutations are usually assumed to occur instantaneously, namely, to be viewed as impulses.…”
Section: Introductionmentioning
confidence: 99%