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English(EN) Non-Asymptotic Bounds for Closed-Loop Identification of Sub-Exponentially Growing Nonlinear Stochastic Systems

新研究详细介绍了非线性随机系统的参数估计界限

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文详细介绍了非线性随机系统闭环辨识中参数估计的非渐近界限。该研究关注具有线性参数化不确定性和加性噪声的不稳定系统，其中控制策略受到探索性输入的扰动。研究结果为特定状态轨迹区域的估计误差建立了非渐近保证，如果整个状态空间具有信息量，则有可能在所有时间上实现高概率保证。 AI

排序理由 发表在arXiv上的研究论文，详细介绍了系统辨识的理论界限。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新研究详细介绍了非线性随机系统的参数估计界限

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发表在arXiv上的研究论文，详细介绍了系统辨识的理论界限。[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) · Seth Siriya, Jingge Zhu, Dragan Ne\v{s}i\'c, Ye Pu ·

    非渐近界限用于指数增长以下的非线性随机系统的闭环辨识

    arXiv:2412.04157v2 Announce Type: replace-cross Abstract: We investigate the problem of least squares parameter estimation from single-trajectory data for discrete-time, unstable, closed-loop nonlinear stochastic systems. Specifically, we consider nonlinear systems with linearly …

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