k-Winner-Take-All Competition Based on Novel Dynamic Neural Networks
Cao, Xinwei; Yang, Yiguo; Li, Shuai; Katsikis, Vasilios N. (2025-10-06)
IEEE
06.10.2025
X. Cao, Y. Yang, S. Li and V. N. Katsikis, "k-Winner-Take-All Competition Based on Novel Dynamic Neural Networks," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 12, pp. 9255-9265, Dec. 2025, doi: 10.1109/TSMC.2025.3614997
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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202604282836
https://urn.fi/URN:NBN:fi:oulu-202604282836
Tiivistelmä
Abstract
The k -winner-takes-all ( k -WTA) problem involves selecting the top k agents with the highest inputs from a set of n candidates. This problem plays a fundamental role in modeling competitive behaviors in social systems and economic environments. In this article, we propose a structurally simplified dynamic neural network to solve the k -WTA problem efficiently. The original k -WTA task is first reformulated as a constrained quadratic programming (QP) problem. A smooth sigmoid function is then introduced to encode inequality constraints implicitly, simplifying the representation. Based on this formulation, we develop a continuous-time neural dynamic model capable of solving the problem in real time. The proposed model is theoretically proven to achieve global convergence and optimality with respect to the k -WTA solution. Extensive numerical experiments, including tests on real-world data, validate the effectiveness of the proposed approach, demonstrating fast convergence, robustness, and practical applicability.
The k -winner-takes-all ( k -WTA) problem involves selecting the top k agents with the highest inputs from a set of n candidates. This problem plays a fundamental role in modeling competitive behaviors in social systems and economic environments. In this article, we propose a structurally simplified dynamic neural network to solve the k -WTA problem efficiently. The original k -WTA task is first reformulated as a constrained quadratic programming (QP) problem. A smooth sigmoid function is then introduced to encode inequality constraints implicitly, simplifying the representation. Based on this formulation, we develop a continuous-time neural dynamic model capable of solving the problem in real time. The proposed model is theoretically proven to achieve global convergence and optimality with respect to the k -WTA solution. Extensive numerical experiments, including tests on real-world data, validate the effectiveness of the proposed approach, demonstrating fast convergence, robustness, and practical applicability.
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