文章基本信息

标题：Quasi-random-sampling high dimensional model representations for the construction of reduced discrete time state space dynamic models
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作者：Romain S.C. Lambert ; Romain S.C. Lambert ; Nilay Shah 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2010
卷号：2
期号：6
页码：7696-7697
DOI：10.1016/j.sbspro.2010.05.184
语种：English
出版社：Elsevier
摘要：AbstractIn the context of real time model-based applications, complex high fidelity models may be computationally too expensive. Model order reduction and system identification techniques have been employed to transform complex models into equivalent reduced order models. However, most of the literature on model order reduction concerns linear time invariant dynamic systems, and the research into non linear model reduction is still on early stage. In this paper, we present a novel approach using quasi random sampling – high dimensional model representation (QRS-HDMR) to generate reduced discrete time state space dynamic models. The approach has the advantages of being able to handle the high dimensional case and produce affine discrete state space models, readily usable in control engineering applications.
关键词：State Space Models;HDMR;Model Reduction;Linearization