Single-state distributed k-winners-take-all neural network model

Yinyan Zhang; Shuai Li; Xuefeng Zhou; Jian Weng; Guanggang Geng

doi:10.1016/j.ins.2023.119528

Single-state distributed k-winners-take-all neural network model

Yinyan Zhang
, Shuai Li^*
, Xuefeng Zhou
, Jian Weng
, Guanggang Geng

^*Corresponding author for this work

BA56 Cognitive production industry

Jinan University
Guangdong Artificial Intelligence and Digital Economy Laboratory
University of Oulu
VTT (former employee or external)
Guangdong Institute of Intelligent Manufacturing (GDAS)
Guangdong Institute of intelligent technology and technology (GIIST)

Research output: Contribution to journal › Article › Scientific › peer-review

20 Citations (Scopus)

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.

Original language	English
Article number	119528
Journal	Information Sciences
Volume	647
DOIs	https://doi.org/10.1016/j.ins.2023.119528
Publication status	Published - Nov 2023
MoE publication type	A1 Journal article-refereed

Funding

This work is supported in part by the National Natural Science Foundation of China under Grant 62206109 , the Guangdong Key Laboratory of Data Security and Privacy Preserving under Grant 2017B030301004 , the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515010976 , the Young Scholar Program of Pazhou Lab under Grant PZL2021KF0022 , the Science and Technology Program of Guangzhou under Grant 202201010457 . G. Geng is supported by the Pearl River Talents Plan . We would like to thank the reviewers for their valuable comments.

Keywords

Distributed competition
k-winners-take-all
Recurrent neural network

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10.1016/j.ins.2023.119528

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title = "Single-state distributed k-winners-take-all neural network model",

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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author = "Yinyan Zhang and Shuai Li and Xuefeng Zhou and Jian Weng and Guanggang Geng",

year = "2023",

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language = "English",

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T1 - Single-state distributed k-winners-take-all neural network model

AU - Zhang, Yinyan

AU - Li, Shuai

AU - Zhou, Xuefeng

AU - Weng, Jian

AU - Geng, Guanggang

PY - 2023/11

Y1 - 2023/11

N2 - 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.

AB - 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.

KW - Distributed competition

KW - k-winners-take-all

KW - Recurrent neural network

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DO - 10.1016/j.ins.2023.119528

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JO - Information Sciences

JF - Information Sciences

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ER -

Single-state distributed k-winners-take-all neural network model

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