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Robust k-WTA Network Generation, Analysis, and Applications to Multiagent Coordination
Qi, Yimeng1,2,3; Jin, Long1,2,4; Luo, Xin5; Shi, Yang6; Liu, Mei3,5
2021-06-16
摘要In this article, a robust k-winner-take-all (k-WTA) neural network employing the saturation-allowed activation functions is designed and investigated to perform a k-WTA operation, and is shown to possess enhanced robustness to disturbance compared to existing k-WTA neural networks. Global convergence and robustness of the proposed k-WTA neural network are demonstrated through analysis and simulations. An application studied in detail is competitive multiagent coordination and dynamic task allocation, in which k active agents [among m (m > k)] are allocated to execute a tracking task with the static m-k ones. This is implemented by adopting a distributed k-WTA network with limited communication, aided with a consensus filter. Simulation results demonstrating the system's efficacy and feasibility are presented.
关键词Task analysis Robustness Biological neural networks Resource management Dynamic scheduling Convergence Recurrent neural networks Consensus filter k-winner-take-all (k-WTA) multiagent coordination theoretical analysis
DOI10.1109/TCYB.2021.3079457
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
页码13
通讯作者Jin, Long(jinlongsysu@foxmail.com) ; Luo, Xin(luoxin21@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000732183800001
语种英语