Gain-Scheduled Finite-Time Synchronization for Reaction–Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains

Song, Xiaona; Man, Jingtao; Song, Shuai; Ahn, Choon Ki

doi:10.1109/tnnls.2020.3009081

Cited by 64 publications

(10 citation statements)

References 49 publications

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“…The derivative of observed disturbance is Applying the new disturbance observer to synchronization between the master and slave systems first requires a form of Eq. (15). This study uses the derivative of the error state between master and slave system to obtain the goals.…”

Section: A Adaptive Fuzzy Disturbance Observermentioning

confidence: 99%

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Synthetic Adaptive Fuzzy Disturbance Observer and Sliding-Mode Control for Chaos-Based Secure Communication Systems

2021

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Section: A Adaptive Fuzzy Disturbance Observermentioning

confidence: 99%

“…The application of synchronization for a network system has been investigated by papers [11][12][13][14]. The synchronization of memristive neural network systems was presented in [15][16]. The synchronization of electronic circuits is found in [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Adaptive Fuzzy Disturbance Observer and Sliding-Mode Control for Chaos-Based Secure Communication Systems

2021

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“…For inequality (25), by exchanging the summation order and utilizing the equal ratio summation formula, one can deduce that…”

Section: A Stabilization and Performance Analysismentioning

confidence: 99%

H _∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism

Wang

Xing

Cao

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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In this article, the H ∞ bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter,

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“…Compared with traditional memristorbased NNs, memristor-based RDNNs can achieve better approximations of real-world systems with the reactiondiffusion phenomenon. Thus, memristor-based RDNNs have attracted increasing attention [12]- [17]. For example, by the Lyapunov stability theorem and Green formula, the global stabilization problem of memristor-based RDNNs has been studied in [12].…”

mentioning

confidence: 99%

“…In [14], the fixed-time passification of memristor-based RDNNs has been investigated based on the Lyapunov method and upper right Dini derivative. In [17], by employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the finite-time synchronization problem has been considered for memristor-based RDNNs.…”

mentioning

confidence: 99%

Event-Triggered Impulsive Fault-Tolerant Control for Memristor-Based RDNNs With Actuator Faults

Zhang

Wang

Park

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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This article focuses on designing an event-triggered impulsive fault-tolerant control strategy for the stabilization of memristor-based reaction-diffusion neural networks (RDNNs) with actuator faults. Different from the existing memristor-based RDNNs with fault-free environments, actuator faults are considered here. A hybrid event-triggered and impulsive (HETI) control scheme, which combines the advantages of event-triggered control and impulsive control, is newly proposed. The hybrid control scheme can effectively accommodate the actuator faults, save the limited communication resources, and achieve the desired system performance. Unlike the existing Lyapunov-Krasovskii functionals (LKFs) constructed on sampling intervals or required to be continuous, the introduced LKF here is directly constructed on event-triggered intervals and can be discontinuous. Based on the LKF and the HETI control scheme, new stabilization criteria are derived for memristor-based RDNNs. Finally, numerical simulations are presented to verify the effectiveness of the obtained results and the merits of the HETI control method.

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Gain-Scheduled Finite-Time Synchronization for Reaction–Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains

Cited by 64 publications

References 49 publications

Synthetic Adaptive Fuzzy Disturbance Observer and Sliding-Mode Control for Chaos-Based Secure Communication Systems

Synthetic Adaptive Fuzzy Disturbance Observer and Sliding-Mode Control for Chaos-Based Secure Communication Systems

H _∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism

Event-Triggered Impulsive Fault-Tolerant Control for Memristor-Based RDNNs With Actuator Faults

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Gain-Scheduled Finite-Time Synchronization for Reaction–Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains

Cited by 64 publications

References 49 publications

Synthetic Adaptive Fuzzy Disturbance Observer and Sliding-Mode Control for Chaos-Based Secure Communication Systems

Synthetic Adaptive Fuzzy Disturbance Observer and Sliding-Mode Control for Chaos-Based Secure Communication Systems

H ∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism

Event-Triggered Impulsive Fault-Tolerant Control for Memristor-Based RDNNs With Actuator Faults

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H _∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism