Identification of continuous-time errors-in-variables models
作者： Kaushik Mahata , Hugues Garnier
DOI: 10.1016/J.AUTOMATICA.2006.04.012
关键词:
摘要: A novel direct approach for identifying continuous-time linear dynamic errors-in-variables models is presented in this paper. The effects of the noise on state-variable filter outputs are analyzed. Subsequently, a few algorithms to obtain consistent parameter estimates framework derived. It also possible design search-free within our framework. can be used non-uniformly sampled data. asymptotic distributions performances proposed illustrated with some numerical simulation examples.
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