Global Asymptotic Stability and Synchronization of Fractional-Order Reaction–Diffusion Fuzzy BAM Neural Networks with Distributed Delays via Hybrid Feedback Controllers

Syed Ali, M.; Stamov, Gani; Stamova, Ivanka; Ibrahim, Tarek F.; Dawood, Arafa A.; Osman Birkea, Fathea M.

doi:10.3390/math11204248

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Synchronization, a ubiquitous and fascinating collective behavior in complex networks, has been extensively studied through the past decades [1][2][3][4][5][6][7][8]. In reality, the complex network may be split into a few subnetworks called clusters due to some partition laws.…”

Section: Introductionmentioning

confidence: 99%

Clustering Component Synchronization of Nonlinearly Coupled Complex Networks via Pinning Control

Liu,

Sun

2024

Mathematics

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In this paper, the problem of clustering component synchronization of nonlinearly coupled complex networks with nonidentical nodes and asymmetric couplings is investigated. A pinning control strategy is designed to achieve the clustering component synchronization with respect to the specified components. Based on matrix analysis and stability theory, clustering component synchronization criteria are established. Two numerical simulations are also provided to show the effectiveness of the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Clustering Component Synchronization of Nonlinearly Coupled Complex Networks via Pinning Control

Liu,

Sun

2024

Mathematics

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“…As a flexible, interpretable machine learning model, fuzzy neural networks (FNNs) have been widely used in various fields, such as image processing [1], fuzzy control [2,3], ranking challenges, risks and threats [4], actual classification and prediction [5][6][7][8], and so on. One of the most commonly used FNN structures is the Takagi-Sugeno-Kang (TSK) [9] fuzzy system, also called TSK neuro-fuzzy system because it can be represented as a neural network [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Zhang,

Wang,

Gong

et al. 2023

Mathematics

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Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both “intraexpert” and “interexpert” variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model.

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Firing Modes and Predefined-time Chaos Synchronization of Novel Memristor-coupled Heterogeneous Neuron

Jia Mei-Mei,

Cao Jia-Wei,

Bai Ming-Ming

2024

Acta Phys. Sin.

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The processing and transmission of biological neural information are realized via firing activities of neurons in different brain regions. Memristors, due to their nanoscale size, non-volatility and synapse-like plasticity, are regarded as ideal devices for emulating biological synapses. Hence, investigating firing modes of memristor-coupled heterogeneous neurons is significant. This paper focuses on modelling, firing modes and chaos synchronization of a memristor-coupled heterogeneous neuron. First, a novel locally active memristor is proposed, and its frequency characteristics, local activity and non-volatility are analyzed. Then, the novel locally active memristor is introduced into the two-dimensional HR neuron and the two-dimensional FHN neuron to construct a novel memristor-coupled heterogeneous neuron model. In numerical simulations, by changing a coupling strength, it is found that the model exhibits the periodic spiking firing mode, the chaotic spiking firing mode, the periodic bursting firing mode and the random bursting firing mode. Besides, the dynamic behavior of the novel memristor-coupled heterogeneous neuron can switch between periodic and chaotic behaviors by changing the initial state. Finally, based on the Lyapunov stability theory and the predefined-time stability theory, a novel predefined-time synchronization strategy is proposed and applied to achieve chaos synchronization of the novel memristor-coupled heterogeneous neuron. The results show that compared with a finite-time synchronization strategy, a fixed-time synchronization strategy and a traditional predefined-time synchronization strategy, the actual convergence time of the novel predefined-time synchronization strategy is the shortest. Studying the firing modes and chaos synchronization of the novel memristor-coupled heterogeneous neuron is helpful to explore the neural functions of the brain and has important significances in the neural signal processing and secure communication fields.

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Global Asymptotic Stability and Synchronization of Fractional-Order Reaction–Diffusion Fuzzy BAM Neural Networks with Distributed Delays via Hybrid Feedback Controllers

Cited by 3 publications

References 44 publications

Clustering Component Synchronization of Nonlinearly Coupled Complex Networks via Pinning Control

Clustering Component Synchronization of Nonlinearly Coupled Complex Networks via Pinning Control

First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Firing Modes and Predefined-time Chaos Synchronization of Novel Memristor-coupled Heterogeneous Neuron

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