Synchronization and settling-time estimation of fuzzy memristive neural networks with time-varying delays: Fixed-time and preassigned-time control

Wang, Leimin; Li, Haoyu; Hu, Cheng; Hu, Junhao; Wang, Qingyi

doi:10.1016/j.fss.2023.108654

Cited by 2 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark 7. Specifically, when the QVFMNNs (1) and ( 3) are simplified into real-valued [4] or complex-valued [29] NN models, the control strategies outlined in this paper can also be adapted for use with real or complex-variable control laws. Additionally, the synchronization conditions established herein are applicable to systems with either real or complex variables.…”

Section: Petsmentioning

confidence: 99%

“…This form of neural network holds immense potential for propelling the evolution of artificial intelligence and heralding a novel technological revolution. Currently, researchers in the field of FMNN have produced several excellent results [1][2][3][4]. For instance, in [1], an impulsive sampled data communication mechanism is proposed to investigate the anti-synchronization of FMNNs.…”

Section: Introductionmentioning

confidence: 99%

“…Gong et al presented the T-S FMNN synchronization criterion in [3]. Additionally, Wang et al investigated the fixed-time and preassigned-time synchronization of FMNNs [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fixed/Preassigned-Time Synchronization of Fuzzy Memristive Fully Quaternion-Valued Neural Networks Based on Event-Triggered Control

Jia,

Hu,

Jiang

2024

Mathematics

Self Cite

View full text Add to dashboard Cite

In this paper, the fixed-time and preassigned-time synchronization issues of fully quaternion-valued fuzzy memristive neural networks are studied based on the dynamic event-triggered control mechanism. Initially, the fuzzy rules are defined within the quaternion domain and the relevant properties are established through rigorous analysis. Subsequently, to conserve resources and enhance the efficiency of the controller, a kind of dynamic event-triggered control mechanism is introduced for the fuzzy memristive neural networks. Based on the non-separation analysis, fixed-time and preassigned-time synchronization criteria are presented and the Zeno phenomenon under the event-triggered mechanism is excluded successfully. Finally, the effectiveness of the theoretical results is verified through numerical simulations.

show abstract

Section: Petsmentioning

confidence: 99%