@article{48eb79b1674841f8a8cf2e24ffefd89e, title = "Optimal Nonparametric Identification from Arbitrary Corrupt Finite Time Series", abstract = "In this paper we formulate and solve a worst-case sys- tem identification problem for single-input, single-output, linear, shift-invariant, distributed parameter plants. The available a priori information in this problem consists of time-dependent upper and lower bounds on the plant impulse response and the additive output noise. The available a posteriori information consists of a corrupt finite output time series obtained in response to a known, nonzero, but otherwise arbitrary, input signal. We present a novel identification method for this problem. This method maps the available a priori and a posteriori information into an “uncertain model{"} of the plant, which is comprised of a nominal plant model, a bounded additive output noise, and a bounded additive model uncertainty. The upper bound on the model uncertainty is explicit and expressed in terms of both the l1and H∞ system norms. The identification method and the nominal model possess certain well-defined optimality properties and are computationally simple, requiring only the solution of a single linear programming problem. {\textcopyright} 1995 IEEE", author = "Jie Chen and Nett, {Carl N.} and Fan, {Michael K.H.}", year = "1995", month = apr, doi = "10.1109/9.376090", language = "English", volume = "40", pages = "769--776", journal = "IEEE Transactions on Automatic Control", issn = "0018-9286", publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC", number = "4", }