Titlebook: Robustness in Identification and Control; A. Garulli (Assistant Professor),A. Tesi (Assistan Conference proceedings 1999 Springer-Verlag L

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蹒跚

Comments on model validation as set membership identification,gested in the “Identification-for-robust-control” context and two more classical statistical tests. By defining the set of models that would pass the chosen model validation test, we may interpret each of these as a set membership identification method. The consequences of such a viewpoint are discu

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SM identification of model sets for robust control design from data,d and the effects of unmodeled dynamics have to be accounted for. The paper presents a unified view of the Set Membership Identification Theory (SMIT), as recently evolved by the authors and coworkers, aiming to deliver not a single model of the system to be identified, but a set of models, indicate

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Hemoptysis

Semi-parametric methods for system identification,ametric components and non-parametric components. The non-parametric components do not have a natural parameterization that is known or suggested from an analytical understanding of the underlying process. These include static nonlinear maps and noise models..We suggest a novel procedure for identif

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Modeling and validation of nonlinear feedback systems,o modeling, identification and fault detection. Prior theoretical and application work in the area of model validation for robust control models focussed on linear fractional models. In this paper we discuss the extension of these methods to certain classes of nonlinear models. The Moore-Greitzer mo

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Towards a harmonic blending of deterministic and stochastic frameworks in information processing,n processing, including identification, signal processing, communications, system design, etc. We begin with a discussion on distinctive features of the two frameworks and explanation of compelling reasons and motivating issues for introducing such a combined framework. Using persistent identificati

Anonymous

Suboptimal conditional estimators for restricted complexity set membership identification,res the solution of complex optimization problems. This paper studies different classes of suboptimal estimators and provides tight upper bounds on their identification error, in order to assess the reliability level of the identified models. Results are derived for fairly general classes of sets an