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- W2809760328 abstract "Equation error, also known as one-step-ahead prediction error, is a common quality-of-fit metric in dynamical system identification and learning. In this letter, we use Lagrangian relaxation to construct a convex upper bound on equation error that can be optimized over a convex set of nonlinear models that are guaranteed to be contracting, a strong form of nonlinear stability. We provide theoretical results on the tightness of the relaxation, and show that the method compares favorably to established methods on a variety of case studies." @default.
- W2809760328 created "2018-07-10" @default.
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- W2809760328 date "2019-01-01" @default.
- W2809760328 modified "2023-09-24" @default.
- W2809760328 title "Convex Bounds for Equation Error in Stable Nonlinear Identification" @default.
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- W2809760328 doi "https://doi.org/10.1109/lcsys.2018.2852266" @default.
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