Optimal Nonparametric Identification from Arbitrary Corrupt Finite Time Series
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 769-776 |
Journal / Publication | IEEE Transactions on Automatic Control |
Volume | 40 |
Issue number | 4 |
Publication status | Published - Apr 1995 |
Externally published | Yes |
Link(s)
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. © 1995 IEEE
Citation Format(s)
Optimal Nonparametric Identification from Arbitrary Corrupt Finite Time Series. / Chen, Jie; Nett, Carl N.; Fan, Michael K.H.
In: IEEE Transactions on Automatic Control, Vol. 40, No. 4, 04.1995, p. 769-776.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review