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• W4385988982 endingPage "119528" @default.
• W4385988982 startingPage "119528" @default.
• W4385988982 abstract "Distributed k-winners-takes-all (k-WTA) neural network (k-WTANN) models have better scalability than centralized ones. In this work, a distributed k-WTANN model with a simple structure is designed for the efficient selection of k winners among a group of more than k agents via competition based on their inputs. Unlike an existing distributed k-WTANN model, the proposed model does not rely on consensus filters, and only has one state variable. We prove that under mild conditions, the proposed distributed k-WTANN model has global asymptotic convergence. The theoretical conclusions are validated via numerical examples, which also show that our model is of better convergence speed than the existing distributed k-WTANN model." @default.
• W4385988982 created "2023-08-19" @default.
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• W4385988982 date "2023-11-01" @default.
• W4385988982 modified "2023-10-14" @default.
• W4385988982 title "Single-state distributed k-winners-take-all neural network model" @default.
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• W4385988982 doi "https://doi.org/10.1016/j.ins.2023.119528" @default.
• W4385988982 hasPublicationYear "2023" @default.
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