Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees
–Neural Information Processing Systems
This paper focuses on the system identification of an important class of nonlinear systems: nonlinear systems that are linearly parameterized, which enjoy wide applications in robotics and other mechanical systems. We consider two system identification methods: least-squares estimation (LSE), which is a point estimation method; and set-membership estimation (SME), which estimates an uncertainty set that contains the true parameters. We provide non-asymptotic convergence rates for LSE and SME under i.i.d.
Neural Information Processing Systems
Mar-21-2026, 18:44:17 GMT
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