Quasi-Synchronization of Time Delay Markovian Jump Neural Networks With Impulsive-Driven Transmission and Fading Channels

Rao, Hongxia; Xu, Yong; Peng, Hui; Lu, Renquan; Su, Chun‐Yi

doi:10.1109/tcyb.2019.2941582

Cited by 50 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, compared with some papers that do not consider reaction–diffusion terms and the LKF that have triple and quadruple integral terms or just use Jensen inequality and some other simple manipulations, e.g. [12–16, 35, 42], the calculation in this paper is more complex.…”

Section: Resultsmentioning

confidence: 99%

“…All of the above factors imply that this kind of systems cannot be described by linear time‐invariant system. To ensure the accuracy of system modelling, in recent years, many scholars have adopted Markovian jump models to describe the complex systems [12–16, 33]. For another, any movement in the real world takes place in a certain spatiotemporal environment, such as wave propagation, chemical reaction process, food web etc., thus the mathematical model established with ignoring the spatial factors is not accurate.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…In NNs, the latching phenomenon is common, which can be controlled effectively if we extract the finite‐state representation from the trained network. It has been proven that the switching between different NNs' modes can be governed by a Markov chain, which is suitable for modelling real systems with unpredicted abrupt changes in the structure and parameters, sudden environmental disturbances and component failures [12–15]. Thus recently, several results that combining INNs and Markovian jump parameters have been appeared, such as [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

Song

Man

Song

et al. 2020

IET Control Theory & Appl

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

Song

Man

Song

et al. 2020

IET Control Theory & Appl

View full text Add to dashboard Cite

“…Definition 1 The AEES (12) reaches asymptotic stability based on the estimator (4), if the following equation holds [39]:…”

Section: Augmented Estimation Error Systemmentioning

confidence: 99%

Impulsive State Estimation for Nonlinear Systems with Redundant Communication Channels and Markov Delay

Rao¹,

Guo²,

Kuang³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The state estimation issue for the discrete-time nonlinear systems with Markov delay is investigated in this paper, where the redundant communication channel is considered to ensure the reliability of transmission. Because the channel capacity is limited, the packet dropout conditions of the main channel and the redundant channel are described by the Bernoulli stochastic variables. In addition, a mode-dependent estimator is proposed based on the current state and the delayed state, simultaneously. Combining with the impulsive control strategy, the efficiency of estimator is improved. An augmented estimation error system is proposed to deal with the Markov delay in the nonlinear system, subsequently, a sufficient condition that ensures the asymptotic stability of the augmented error system is obtained by a constructed Lyapunov functional candidate and the gains of the impulsive estimator are derived. Finally, a numerical example of moving vehicle is utilized to illustrate the developed results.

show abstract

“…However, connection weights of FMNNs changed according to the state dynamics and the synchronization of derive-response systems will be destroyed. When the parameter mismatches are small enough, we can control the synchronization error in a small region, which is called quasi-synchronization [21][22][23][24]. Recently, the impulsive control method has been widely used in the quasi-synchronization of chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Synchronization of Nonidentical Fractional-Order Memristive Neural Networks via Impulsive Control

Chen

Zhao

2021

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

This paper investigates the quasi-synchronization of nonidentical fractional-order memristive neural networks (FMNNs) via impulsive control. Based on a newly provided fractional-order impulsive systems comparison lemma, the average impulsive interval definition, and the Laplace transform, some quasi-synchronization conditions are obtained with fractional order 0 < α < 1 . In addition, the error convergence rates and error boundary are also obtained. Finally, one simulation example is presented to show the validity of our results.

show abstract

Quasi-Synchronization of Time Delay Markovian Jump Neural Networks With Impulsive-Driven Transmission and Fading Channels

Cited by 50 publications

References 47 publications

Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

Impulsive State Estimation for Nonlinear Systems with Redundant Communication Channels and Markov Delay

Quasi-Synchronization of Nonidentical Fractional-Order Memristive Neural Networks via Impulsive Control

Contact Info

Product

Resources

About