文章基本信息

标题：Improving Linear State-Space Models with Additional Iterations
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作者：Suat Gumussoy ; Ahmet Arda Ozdemir ; Tomas McKelvey 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：341-346
DOI：10.1016/j.ifacol.2018.09.158
语种：English
出版社：Elsevier
摘要：AbstractAn estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation can be improved by subsequent re-estimation of theB,C, andDmatrices (which involves linear estimation problems). Several tests are performed, which show that it is generally advisable to do such further re-estimation steps using the maximum likelihood criterion. Stated more succinctly in terms of MATLAB® functions, ssest generally outperforms n4sid.
关键词：KeywordsParameter estimationState-space modelsSubspace identificationMaximum Likelihood