English(EN) Learning Maximal Regions of Attraction on Compact Sets using Taylor Lyapunov Networks

新型泰勒李雅普诺夫网络学习最大吸引域

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种使用神经网络计算非线性动力学系统最大吸引域的新颖方法。该方法通过在紧凑的计算域上直接推导李雅普诺夫和祖波夫定理，解决了现有方法的局限性。新的泰勒李雅普诺夫网络架构旨在通过构造保证有效的李雅普诺夫函数，并被证明是紧凑域上这些函数的通用逼近器。 AI

影响引入了一种用于分析非线性动力学系统的新型神经网络架构，可能对控制理论和机器人学研究产生影响。

排序理由学术论文，详细介绍了一种学习动力学系统属性的新方法和架构。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Matthieu Barreau, Nicola Bastianello · 2026-11-02 05:00

Learning Maximal Regions of Attraction on Compact Sets using Taylor Lyapunov Networks

arXiv:2408.17246v3 Announce Type: replace-cross Abstract: We consider the problem of learning maximal Lyapunov functions to compute the maximal region of attraction of nonlinear dynamical systems using neural networks. Two obstacles limit existing neural-network approaches: 1) fi…