Stability of inertial delayed neural networks with stochastic delayed impulses via matrix measure method

Cui, Qian; Li, Lulu; Cao, Jinde

doi:10.1016/j.neucom.2021.10.113

Cited by 15 publications

(7 citation statements)

References 33 publications

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Section: Numerical Simulationsmentioning

confidence: 99%

“…Remark The stability results of some types of INNs were given in [33–35, 39] and [44] without using state‐dependent switching method. However, the stability results of SSINNs () and SSINNs () are considered in this paper using state‐dependent switching method, therefore, our results are significant over the results obtained in the previous works [33–35, 39, 44] and the stability results of SSNNs without inertial term are presented in [49, 57] and [58]. In [57] and [58], authors studied the stability of stochastic switched NNs with parameter uncertainties with random disturbances alone or the stability analysis of stochastic switched NNs with time‐varying delay alone.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…The term "inertial" is viewed as a critical tool and is used to describe complicated bifurcation behavior and chaos. Thus, INNs have more practical applications in areas like autonomous control, predictive estimation, and signal processing and have attracted wide attention; for details, see [32][33][34][35][36][37][38][39][40][41][42][43][44]. In particular, finite-time stabilization for switched inertial neural networks (SINNs) with time delay is investigated in [32] using a variable transformation method and LKF with a delayed or non-delayed nonlinear feedback controller.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Banu¹,

Banupriya²,

Krishnasamy

et al. 2023

Math Methods in App Sciences

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In this paper, the problem of robust stability analysis for a type of uncertain stochastic switched inertial neural networks (SSINNs) with time-varying delay is investigated. First, the original second-order system is converted into first-order differential equations using the variable transformation method. Next, some sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained using Lyapunov-Krasovskii functional (LKF), state-dependent switching (SDS) method, and Jensen's integral inequality for estimating integral inequalities so that the augmented system is robust, global, and asymptotically stable in the mean square for the uncertain SSINNs with time-varying delay. It is shown that the stability of the above considered system composed of all unstable subsystems can be achieved by using the SDS law. Finally, two numerical simulations are provided to demonstrate the effectiveness of the proposed SDS law.

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Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Banu¹,

Banupriya²,

Krishnasamy

et al. 2023

Math Methods in App Sciences

View full text Add to dashboard Cite

show abstract

“…• Compared with [26,27], this paper further considers the influence of inertia term in the FNN model, inertia term can better describe the relationship between current and flux. Compared with [20][21][22][23], fuzzy logic is considered in this paper, it improves the ability to handle the fuzziness in simulations.…”

Section: Introductionmentioning

confidence: 99%

“…(22) and(23), we can obtain the following two transcendental Equations 5 π2 exp(0.3Λ 3 ) + exp(−0.24)…”

mentioning

confidence: 99%

Robustness analysis of exponential stability of fuzzy inertial neural networks

Fang

Xie

2023

Preprint

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This paper characterizes the robustness of exponential stability of fuzzy inertial neural network which contains time delays or stochastic disturbance through the estimation of upper limits of perturbations. By utilizing Gronwall-Bellman lemma as well as some inequality techniques, the limits of both time delays and stochastic disturbances are derived in this paper which can make the disturbed system keep exponential stability. And the constraints between the two types of disturbances are provided in this paper. Examples are offered to validate our results.

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Aperiodically Intermittent Control for Exponential Stabilization of Delayed Neural Networks Via Time-dependent Functional Method

Fan

Huang

Wang

et al. 2022

Neural Process Lett

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Stability of inertial delayed neural networks with stochastic delayed impulses via matrix measure method

Cited by 15 publications

References 33 publications

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Robustness analysis of exponential stability of fuzzy inertial neural networks

Aperiodically Intermittent Control for Exponential Stabilization of Delayed Neural Networks Via Time-dependent Functional Method

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