Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches

Fu, Qianhua; Zhong, Shouming; Shi, Kaibo

doi:10.1016/j.amc.2021.126169

Cited by 11 publications

(2 citation statements)

References 44 publications

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“…Relying on the pinning sampled-data control principle, u i2 (t) is a pinning sampled-data control intended to apply to the first l nodes 0 ≤ i ≤ l. The selected or unselected pinning nodes don't base on the estimation of node errors, where one avoids rearranging each node errors. For further study, there is another technique which doesn't base on the estimation of node errors in the reference [66].…”

Section: A Synchronization Analysis With Sample-data Controlmentioning

confidence: 99%

Dissipative Pinning Sampled-Data Control for Function Projective Synchronization of Neural Networks With Hybrid Couplings and Time-Varying Delays

et al. 2022

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This paper is concerned with the dissipative problem based pinning sampled-data control scheme. We investigate the problem for function projective synchronization of neural networks with hybrid couplings and time-varying delays. The main purpose is focused on designing a pinning sampled-data function projective synchronization controller such that the resulting function projective synchronization neural networks are stable and satisfy a strictly H ∞ , L 2 − L ∞ , passivity and dissipativity performance by setting parameters in the general performance index. It is assumed that the parameter uncertainties are norm-bounded. By construction of an appropriate Lyapunov-Krasovskii containing single, double and triple integrals, which fully utilize information of the neuron activation function and use refined Jensen's inequality for checking the passivity of the addressed neural networks are established in linear matrix inequalities (LMIs). This result is less conservative than the existing results in literature. It can be checked numerically using the effective LMI toolbox in MATLAB. Numerical examples are provided to demonstrate the effectiveness and the merits of the proposed methods. INDEX TERMS dissipative, function projective synchronization, neural networks, time-varying delays, sampled-data control.

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Section: A Synchronization Analysis With Sample-data Controlmentioning

confidence: 99%

Dissipative Pinning Sampled-Data Control for Function Projective Synchronization of Neural Networks With Hybrid Couplings and Time-Varying Delays

et al. 2022

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“…Impulsive sequences are called to be stabilizing impulses if they can promote the stability of differential systems, while destabilizing impulses can suppress the stability of differential systems. Stability and synchronization problems of a dynamical system with different categories of impulses have become an interesting research topic, and some results with respect to the problems have been reported in [8][9][10][11][12][13][14][15][16]. For instance, in [8], the asymptotic stability of impulsive recurrent neural networks with stochastic disturbances and time delays was discussed by virtue of the Lyapunov functional approach and LMI technique.…”

Section: Introductionmentioning

confidence: 99%

Input-to-State Stability Analysis for Stochastic Mixed Time-Delayed Neural Networks with Hybrid Impulses

Zhao

Song

Zhu

et al. 2022

Mathematical Problems in Engineering

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This paper investigates the mean-square exponential input-to-state stability (MEISS) of stochastic mixed time-delayed neural networks with hybrid impulses. A generalized comparison principle is introduced and a new inequality about the solution of an impulsive differential equation is established. Moreover, by utilizing the proposed inequality and average impulsive interval approach based on different kinds of impulsive sequences, some novel criteria on MEISS are established. When the external input is removed, several conclusions on mean-square exponential stability (MES) are also derived. Unusually, the hybrid impulses including destabilizing and stabilizing impulses have been taken into account in the presented system. Finally, two simulation examples are provided to demonstrate the validity of our theoretical results.

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The Boundedness and the Global Mittag-Leffler Synchronization of Fractional-Order Inertial Cohen–Grossberg Neural Networks with Time Delays

Zhang

2021

Neural Process Lett

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Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches

Cited by 11 publications

References 44 publications

Dissipative Pinning Sampled-Data Control for Function Projective Synchronization of Neural Networks With Hybrid Couplings and Time-Varying Delays

Dissipative Pinning Sampled-Data Control for Function Projective Synchronization of Neural Networks With Hybrid Couplings and Time-Varying Delays

Input-to-State Stability Analysis for Stochastic Mixed Time-Delayed Neural Networks with Hybrid Impulses

The Boundedness and the Global Mittag-Leffler Synchronization of Fractional-Order Inertial Cohen–Grossberg Neural Networks with Time Delays

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