Journal article

Non-asymptotic confidence regions for model parameters in the presence of unmodelled dynamics

Marco C Campi, Sangho Ko, Erik Weyer



This paper deals with the problem of constructing confidence regions for the parameters of truncated series expansion models. The models are represented using orthonormal basis functions, and we extend the 'Leave-out Sign-dominant Correlation Regions' (LSCR) algorithm such that non-asymptotic confidence regions for the parameters can be constructed in the presence of unmodelled dynamics. The constructed regions have guaranteed probability of containing the true parameters for any finite number of data points. The algorithm is first developed for FIR models and then extended to models with generalized orthonormal basis functions. The usefulness of the developed approach is demonstrated for FI..

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Awarded by Australian Research Council

Funding Acknowledgements

The research of M. C. Campi was supported by MIUR under the project ``Identification and Adaptive Control of Industrial Systems''. The research of S. Ko and E. Weyer was supported by the Australian Research Council under the Discovery Grant Scheme, Project DP0558579. The material in this paper was partially presented at the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, Dec 12-14, 2007. This paper was recommended for publication in revised form by Associate Editor Wolfgang Scherrer under the direction of Editor Torsten Soderstrom.