By J. P. Norton (auth.), Mario Milanese, John Norton, Hélène Piet-Lahanier, Éric Walter (eds.)
In reaction to the turning out to be curiosity in bounding errors ways, the editors of this quantity supply the 1st number of papers to explain advances in concepts and functions of bounding of the parameters, or country variables, of doubtful dynamical structures. participants discover the applying of the bounding process as a substitute to the probabilistic research of such platforms, referring to its significance to powerful control-system design.
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Extra info for Bounding Approaches to System Identification
20 similar results are derived for more general classes of information. (36) Another criterion is to minimize the volume of FPSy-(29) In Ref. 29 a recursive selection procedure is given based on heuristics to avoid poor choices without guaranteeing the best. Characterization is given of the minimum number of sampling times assuring minimum volume of the feasible parameter set FPSv for y = 5lA in Ref. 37. 2. Reduced Order Models In the previous sections, it is supposed that the structure of the problem is known, for example the number of autoregressive and moving-average terms for an ARMA model.
Then, two types of projection algorithms are described, and their link with the EOB algorithms is established. After that, the EOB algorithms are interpreted as robust identification algorithms with a dead zone. The performance of these algorithms is compared through computer simulations where the influence of the choice of the a priori error bound is more particularly studied. 1. INTRODUCTION In practice, the identification of a parametric model from measured signals must include both the estimation of the model parameters and an evaluation of the estimated parameter uncertainty.
8. M. K. Smit, Measurementl, 181 (1983). 9. E. Walter and H. Piet-Lahanier,Proceedings ofthe 25th IEEE Conference on Decision and Control, Athens (1986). 10. M. Milanese and A. Vicino, Automatica 27, 403 (1991). II. J. G. Ecker, SIAM Review 1, 339 (1980). 12. J. E. Falk, Tech. Rep. T-274, George Washington University, Washington DC (1973). 26 M. MILANESE AND A. VICINO 13. M. Milanese and A. Vicino, in: Robust Estimation and Exact Uncertainty Intervals Evaluation for Nonlinear Models, of Systems Modelling and Simulation (S.
Bounding Approaches to System Identification by J. P. Norton (auth.), Mario Milanese, John Norton, Hélène Piet-Lahanier, Éric Walter (eds.)