Robustness analysis method for orbit control

Zhang, Jingrui; Yang, Keying; Qi, Rui; Zhao, Shenghui; Li, Yanyan

doi:10.1016/j.actaastro.2017.03.034

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…In the Example 2 of [26], α < 0.0159, which is smaller than 0.1013. This shows that the system (27) has wider range of piecewise argument and implies that the system (27) can withstand higher intensity of impact from time delay or advance.…”

Section: Illustrative Numerical Examplesmentioning

confidence: 91%

“…As a kind of nonlinear dynamic system, the stability of recurrent neural networks (RNNs) mainly rely on its parameter allocation [5,7,18,27]. It is known that arbitrary perturbations and various time delays in the process of neuron activation may led to instability or oscillation of RNNs [3,4,12,14].…”

Section: Introductionmentioning

confidence: 99%

“…describes the stability performance of(27) with σ = 0.04,{α k } = { k 100 }, {η k } = { 2k+1 200 }, k ∈ N. Figure 3 illustrates a degenerative performance of RNN (27) for σ = 1, {α k } = { k 100 }, {η k } = { 2k+1 200 }, k ∈ N.…”

mentioning

confidence: 99%

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Robust Exponential Stability of Recurrent Neural Networks with Deviating Argument and Stochastic Disturbance

Mingxin¹,

Si²,

Xie³

2020

Eur. J. Pure Appl. Math.

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It is well known that deviating argument and stochastic disturbance may derail the stability of recurrent neural networks (RNNs). This paper discusses the robustness of global exponential stability (GES) of RNNs accompanied with deviating argument and stochastic disturbance. For a given global exponentially stable RNNs, it is interesting to know how much the length of the interval of piecewise function and the interference intensity so that the disturbed system may still be exponentially stable. The available upper boundary of the range of piecewise variables and the interference intensity in the disturbed RNNs to keep GES are the solutions of some transcendental equations. Finally, some examples are provided to demonstrate the efficacy of the inferential results.

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Section: Illustrative Numerical Examplesmentioning

confidence: 91%