Titlebook: Bounding Approaches to System Identification; Mario Milanese,John Norton,Éric Walter Book 1996 Springer Science+Business Media New York 19

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Recursive Estimation Algorithms for Linear Models with Set Membership Error,ing the confidence of the obtained estimates is discussed, particularly information required on the noise structure in order to assess the confidence of the estimates is shown..Finally, the possibility of using the algorithms for time-varying system identification is considered and the case of uncertain regressors is addressed.

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https://doi.org/10.1007/978-88-470-1353-7entification algorithms with a dead zone. The performance of these algorithms is compared through computer simulations where the influence of the choice of the . error bound is more particularly studied.

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https://doi.org/10.1007/978-88-470-1353-7 ways, it is possible to recursively compute inner and outer bounds of the uncertainty intervals. It is shown that the inner bound is asymptotically tight. It is demonstrated that transfer function parameter intervals can also be estimated, by applying the method to measured frequency function data.

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Tobacco Prevalence and Treatment,described. A simulation example compares EIV and equation-error bounding. Techniques for shortening the computation of EIV parameter bounds, and for finding polytope and ellipsoid approximations, are given.

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Overview of the Volume,require a collection of papers as a broad introduction to the field and a review of current progress. The basic idea of describing plant uncertainty by bounds is as old as toleranced engineering design. State bounding was introduced to the control engineering community in the late 1960s and paramete

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Optimal Estimation Theory for Dynamic Systems with Set Membership Uncertainty: An Overview, interpolation, smoothing, and functions approximation, one has to evaluate some unknown variable using available data. The data are always associated with some uncertainty and it is necessary to evaluate how this uncertainty affects the estimated variables. Typically, the problem is approached assu

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Solving Linear Problems in the Presence of Bounded Data Perturbations,or instance from measurement or round-off errors, to mention only these two possible sources. The problem of how inaccuracy in data influences results (for instance, how does it affect a quality of system identification or signal recovery) attracts attention not only for obvious practical reasons, b

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