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
Single-state distributed k-winners-take-all neural network model
|Author:||Zhang, Yinyan1,2,3; Li, Shuai4,5; Zhou, Xuefeng6;|
1College of Cyber Security, Jinan University, Guangzhou, 510632, China
2Pazhou Lab, Guangzhou, 510335, China
3Guangdong Key Laboratory of Data Security and Privacy Preserving, Guangzhou, 510632, China
4Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, Oulu, 90570, Finland
5VTT-Technical Research Centre of Finland, Kaitovayla 1, Oulu, 90590, Finland
6Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, 510095, China
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231024141132
|Publish Date:|| 2023-10-24
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
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.
No data was used for the research described in the article.
© 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/).