Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks

Shi, Qing; He, Fenghua; Wu, Jiagui; Chen, Feng

doi:10.1109/access.2020.2966508

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…To overcome this issue, robust incremental adaptive networks with alternative cost functions such as Wilcoxon norm, Huber norm, and maximum correntropy criterion have been reported in [10]- [13]. Similar robust diffusion adaptive networks are available in the literature [14]- [18].…”

Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Estimation Algorithm for Hamiltonian Multi-Agent Networks

Giv

Khalili

Rastegarnia

et al. 2021

IEEE Control Syst. Lett.

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In this paper a robust incremental adaptation algorithm is presented to solve distributed estimation for a Hamiltonian network, where the measurements at each node may be corrupted by heavy-tailed impulsive noise. In the proposed algorithm, each node employs an error-nonlinearity into the update equation to mitigate the detrimental effects of impulsive noise. Moreover, the algorithm estimates both the optimal error non-linearity and the unknown parameter together, which in turn, obviates the requirement of prior knowledge about the statistical characteristics of measurement noise. In addition to algorithm development, its steady-state performance as well as convergence analysis have been provided. Simulation results validate the correctness of the analysis and reveal the superiority of the proposed algorithm over some existing algorithms. Index terms-Adaptive network, Hamilton, incremental, robust estimation.

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Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Estimation Algorithm for Hamiltonian Multi-Agent Networks

Giv

Khalili

Rastegarnia

et al. 2021

IEEE Control Syst. Lett.

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“…• The proposed clustering algorithms enable the vast majority of nodes to remain in a dormant state, thereby reducing energy consumption [174][175][176]. For example, in [174] the authors proposed a temporary cluster-construction algorithm.…”

Section: Clusteringmentioning

confidence: 99%

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

Li²,

Chen³

et al. 2023

Meas. Sci. Technol.

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Target tracking applications are promising, it has great theoretical and practical significance, but the research faces great challenges. With the development of multi-modal depth sensing technology, a large number of scholars have proposed various target tracking methods based on heterogeneous sensing and got great results. This paper provides an overview of the techniques involved in target tracking in the different layers of the network and a comprehensive analysis of the research progress in heterogeneous sensing techniques in each layer. First, this paper introduced the single sensing scheme and heterogeneous sensing scheme in the physical layer. Second, we presented heterogeneous communication technologies and heterogeneous optimization methods for communication protocols in the network layer. Third, we combined several typical heterogeneous sensory target tracking applications and analyzed the application of cloud computing, edge computing, big data, and blockchain technologies. Finally, we discussed the challenges and future direction of heterogeneous sensory target tracking methods.

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“…The role of features with lower orders of magnitude will be relatively weakened. Standardized processing can make the features have the same scale to ensure comparability [9]. The Z-score algorithm is optimized in this paper, and the specific calculation method is shown in Formula 1.…”

Section: Characteristics Of Educational Resource-sharing 21 Similarit...mentioning

confidence: 99%

Primary education resource-sharing based on feature clustering optimization model

Tan

Cao

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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The mode of educational reform and development has undergone profound changes. To turn teaching into a knowledge-intensive specialty, it is necessary to construct an ecological environment that is conducive to the creation, dissemination, sharing, and accumulation of educational resources. This will be conducive to promoting the sharing and flow of educational resources within the organization, as well as the updating of knowledge and the professional development of individual teachers. At the same time, it can also promote the positive transformation of teachers' individual knowledge to organizational knowledge, and promote the systematic construction and optimal development of the organization. This paper integrates information technology and primary educational resources to promote the sharing of high-quality resources to solve the problem of uneven distribution of primary education resources. Feature clustering is integrated into multi-task recommendation, and neighbor subsets are recommended to share real information according to the quality index of nodes. This strategy provides external incentives to the nodes to help them evaluate the trustworthiness of the nodes' information sharing behaviors in their neighbors. In this paper, it is recommended that a subset of nodes with high quality index share information, which plays a role in encouraging efficient information cooperation among nodes. The results tell that the proposed clustering algorithm and the multi-task clustering recommendation algorithm have excellent performance in the network. The proposed algorithm has good robustness and accuracy both in the difference of the parameters to be estimated and in the multi-task environment. The research content of this paper is conducive to the high-quality sharing of primary education resources, and plays a role in promoting the improvement of education level.

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Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks

Cited by 4 publications

References 45 publications

A Robust Adaptive Estimation Algorithm for Hamiltonian Multi-Agent Networks

A Robust Adaptive Estimation Algorithm for Hamiltonian Multi-Agent Networks

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

Primary education resource-sharing based on feature clustering optimization model

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