Li Jiexing, Guan Yongji, Deng Tiantai, Jin Long
School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Physics and Electronic Information Engineering, Qinghai Normal University, Xining, China; College of Computer Science and Engineering, Jishou University, Jishou, China.
School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Neural Netw. 2025 Nov;191:107839. doi: 10.1016/j.neunet.2025.107839. Epub 2025 Jul 8.
For the k-winners-take-all (kWTA) operation, several anti-noise neurodynamic approaches have been investigated to counteract various types of disturbances and uncertainties. However, these approaches still fail to effectively address periodic noise originating from external environmental interference, sensor inaccuracies, or internal system oscillations. To address this issue, a periodic-noise-tolerant neurodynamic (PNTND) approach for kWTA operation is proposed, which exhibits the capability to learn and compensate for errors induced by periodic noise. Additionally, the PNTND approach effectively eliminates interference caused by the aperiodic noise originating from the superposition of periodic noises. Theoretical analyses and numerical simulations reveal the excellent convergence performance of the PNTND approach. Moreover, we construct a social opinion evolution model that incorporates periodic noise interference based on the proposed PNTND approach, thereby demonstrating its practical applicability.
对于胜者全得（kWTA）操作，已经研究了几种抗噪声神经动力学方法来对抗各种类型的干扰和不确定性。然而，这些方法仍然无法有效解决源自外部环境干扰、传感器不准确或内部系统振荡的周期性噪声。为了解决这个问题，提出了一种用于kWTA操作的耐周期性噪声神经动力学（PNTND）方法，该方法具有学习和补偿由周期性噪声引起的误差的能力。此外，PNTND方法有效地消除了由周期性噪声叠加产生的非周期性噪声所引起的干扰。理论分析和数值模拟揭示了PNTND方法出色的收敛性能。此外，我们基于所提出的PNTND方法构建了一个包含周期性噪声干扰的社会舆论演化模型，从而证明了其实际适用性。
