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

hierarchy

書目名稱Robustness in Identification and Control影響因子(影響力)




書目名稱Robustness in Identification and Control影響因子(影響力)學科排名




書目名稱Robustness in Identification and Control網(wǎng)絡公開度




書目名稱Robustness in Identification and Control網(wǎng)絡公開度學科排名




書目名稱Robustness in Identification and Control被引頻次




書目名稱Robustness in Identification and Control被引頻次學科排名




書目名稱Robustness in Identification and Control年度引用




書目名稱Robustness in Identification and Control年度引用學科排名




書目名稱Robustness in Identification and Control讀者反饋




書目名稱Robustness in Identification and Control讀者反饋學科排名

逃避系列單詞

Comments on model validation as set membership identification,chosen model validation test, we may interpret each of these as a set membership identification method. The consequences of such a viewpoint are discussed, and we focus on the important, but perhaps controversial concept of “independence” to make further selections of models within the thus defined sets.

Patrimony

genuine

Modal robust state estimator with deterministic specification of uncertainty, parameters and the noise are individually confined within specified bounds. The basic operations involved in the time and observation updates are examined and the approximations needed to deal with non-linearity, non-convexity and excessive complication of the bounds are considered.

一再困擾

malign

Modeling and validation of nonlinear feedback systems,sed on linear fractional models. In this paper we discuss the extension of these methods to certain classes of nonlinear models. The Moore-Greitzer model of rotating stall is used as a simple example to illustrate the underlying ideas.

Cytokines

conflate

maverick

不出名

SM identification of model sets for robust control design from data,. Optimality and convergence results of algorithms for identifying model sets are reported, related to identification problems with different settings of experimental conditions, noise assumptions and norms measuring the system approximation.