Data di Pubblicazione:
2007
Abstract:
In this paper we consider the problem of constructing confidence
regions for the parameters of nonlinear dynamical systems. The
proposed method uses higher order statistics and extends the LSCR (Leave-out Sign-dominant Correlation Regions) algorithm for linear systems introduced in \cite{CampiWeyer05}.
The confidence regions contain the
true parameter value with a guaranteed probability for any finite
number of data points. Moreover, the confidence regions shrink around the true parameter value as the number of data points increases. The usefulness of the proposed approach is
illustrated on some simple examples.
regions for the parameters of nonlinear dynamical systems. The
proposed method uses higher order statistics and extends the LSCR (Leave-out Sign-dominant Correlation Regions) algorithm for linear systems introduced in \cite{CampiWeyer05}.
The confidence regions contain the
true parameter value with a guaranteed probability for any finite
number of data points. Moreover, the confidence regions shrink around the true parameter value as the number of data points increases. The usefulness of the proposed approach is
illustrated on some simple examples.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Nonlinear Systems; Systems Identification; Guaranteed Confidence Regions; LSCR; higher order statistics
Elenco autori:
Dalai, Marco; E., Weyer; Campi, Marco
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