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Distributed k-winners-take-all via multiple neural networks with inertia

  • Published:2022-07 Issue: Volume:151 Page:385-397
  • ISSN:0893-6080
  • Container-title:Neural Networks
  • language:en
  • Short-container-title:Neural Networks

Author:

Wang Xiaoxuan,Yang Shaofu,Guo ZhenyuanORCID,Huang Tingwen

Funder

Qatar National Research Fund

China Association for Science and Technology

National Natural Science Foundation of China

Hunan Provincial Natural Science Foundation

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference49 articles.

1. Asymptotic stabilization of inertial gradient dynamics with time-dependent viscosity;Attouch;Journal of Differential Equations,2017

2. Graphs and matrices;Bapat,2010

3. Convex analysis and monotone operator theory in Hilbert spaces;Bauschke,2011

4. Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment;Cherukur;Automatica,2016

5. New accurate and flexible design procedure for a stable KWTA continuous time network;Costea;IEEE Transactions on Neural Networks,2011

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1. Distributed Dynamic Task Allocation for Moving Target Tracking of Networked Mobile Robots Using $k$-WTA Network;IEEE Transactions on Neural Networks and Learning Systems;2024

2. Performance Evaluation of Distributed k-WTA on Dynamic Undirected Connected Graphs and Its Application to Task Allocation;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17

3. Single-state distributed k-winners-take-all neural network model;Information Sciences;2023-11

4. A Distributed $k$-Winners-Take-All Model With Binary Consensus Protocols;IEEE Transactions on Cybernetics;2023

5. A Distributed Network System for Nonsmooth Coupled-Constrained Optimization;IEEE Transactions on Network Science and Engineering;2022-09-01
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