Single-state distributed <em>k</em>-winners-take-all neural network model
Zhang, Yinyan; Li, Shuai; Zhou, Xuefeng; Weng, Jian; Geng, Guanggang (2023-08-18)
Zhang, Y., Li, S., Zhou, X., Weng, J., & Geng, G. (2023). Single-state distributed k-winners-take-all neural network model. In Information Sciences (Vol. 647, p. 119528). Elsevier BV. https://doi.org/10.1016/j.ins.2023.119528
© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe20231024141132
Tiivistelmä
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.
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