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- W2811232238 abstract "Abstract The problem of optimal approximate system identification is addressed with a newly defined measure of misfit between observed time series and linear time-invariant models. The behavioral framework is used as a suitable axiomatic setting for a non-parametric introduction of system complexity, and allows for a notion of misfit of dynamical systems that is independent of system representations. The misfit function introduced here is characterized in terms of the induced norm of a Hankel operator associated with the data and a co-inner kernel representation of a model. Sets of Pareto-optimal models are defined as feasible trade-offs between complexity and misfit of models, and it is shown how all Pareto-optimal models are characterized as exact models of compressed data sets obtained from Hankel-norm approximations of data matrices. This leads to new conceptual algorithms for optimal approximate identification of time series. © 1997 Elsevier Science Ltd." @default.
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- W2811232238 date "1996-01-01" @default.
- W2811232238 modified "2023-09-28" @default.
- W2811232238 title "Optimal Hankel norm identification of dynamical systems" @default.
- W2811232238 hasPublicationYear "1996" @default.
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