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New research details parameter estimation bounds for nonlinear stochastic systems

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A new research paper published on arXiv details non-asymptotic bounds for parameter estimation in closed-loop identification of nonlinear stochastic systems. The study focuses on unstable systems with linearly parameterized uncertainty and additive noise, where a control policy is perturbed by exploratory input. The findings establish non-asymptotic guarantees on estimation error for specific state trajectory regions, with potential for high-probability guarantees across all times if the entire state space is informative. AI

RANK_REASON Research paper published on arXiv detailing theoretical bounds for system identification. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New research details parameter estimation bounds for nonlinear stochastic systems

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Research paper published on arXiv detailing theoretical bounds for system identification. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Seth Siriya, Jingge Zhu, Dragan Ne\v{s}i\'c, Ye Pu ·

    Non-Asymptotic Bounds for Closed-Loop Identification of Sub-Exponentially Growing Nonlinear Stochastic Systems

    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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