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新算法简化了分数阶动力学系统辨识

研究人员开发了一种用于辨识离散时间分数阶动力学系统(FoDS)的新算法。该算法通过将其重构为最小二乘估计来简化辨识过程,避免了复杂的多项式方程求解。所提出的方法包括详细的样本复杂度分析,表明估计误差以 O(d/ε) 的速率衰减,其中 d 是状态变量的维度,ε 是所需的精度。仿真结果证实了理论界限,并证明了该算法的准确性和鲁棒性。 AI

影响 这项研究可能导致对复杂系统进行更准确的建模,从而影响依赖于动力学系统分析的领域。

排序理由 该集群包含一篇在 arXiv 上发表的学术论文,详细介绍了新算法及其理论分析。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新算法简化了分数阶动力学系统辨识

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该集群包含一篇在 arXiv 上发表的学术论文,详细介绍了新算法及其理论分析。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaole Zhang, Vijay Gupta, Paul Bogdan ·

    面向离散时间分数阶动力学系统的采样复杂度感知框架

    arXiv:2501.17499v1 Announce Type: cross Abstract: A variety of complex biological, natural and man-made systems exhibit non-Markovian dynamics that can be modeled through fractional order differential equations, yet, we lack sample comlexity aware system identification strategies…