文章基本信息

标题：Optimal Initial State for Fast Parameter Estimation in Nonlinear Dynamical Systems
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作者：Qiaochu Li ; Carine Jauberthie ; Lilianne Denis-vidal 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：20
页码：557-562
DOI：10.1016/j.ifacol.2015.10.200
语种：English
出版社：Elsevier
摘要：AbstractThis paper deals with optimal initial state for parameter estimation in a bounded error context. Based on sensitivity analysis, our original method uses this information to evaluate the optimal initial state start point for precise parameter estimation task. In our framework, the uncertainty (on measurement noise and parameters) is considered as to be interval, thus guaranteed sensitivity analysis method has been applied which ensure to obtain a better parameter estimation. Once the system's initial state is determined, a set inversion procedure combined with a volumetric criterion's contractor is proposed. The proposition of check the necessity of the contractor's action moment is new. Besides, we point out that the set membership computation should have the measurement points as few as possible, an elementary effect analysis in the interval analysis context is also implemented to achieve the fast and guaranteed parameter estimation aim.
关键词：KeywordsState estimationNonlinear systemsContractorBounded noiseInterval analysis