文章基本信息

标题：Identification of nonlinear dynamical system with synthetic data: a preliminary investigation
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作者：M. Mazzoleni ; M. Scandella ; S. Formentin 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：622-627
DOI：10.1016/j.ifacol.2018.09.227
语种：English
出版社：Elsevier
摘要：AbstractThis paper introduces a new rationale for learning nonlinear dynamical systems. The method makes use of an additional identification dataset, obtained without performing a new experiment on the system under study. The data are generated in an automatical manner, starting from a set of experimentally acquired measurements. In order to leverage the additional generated information, fundamental techniques from the machine learning field known as Semi-Supervised Learning (SSL) are employed and adapted. The problem is then cast as a regularized parametric learning problem. The effectiveness of the proposed approach is assessed on various nonlinear benchmark systems via repeated simulations, comparing the obtained results with a standard regularization method for learning parametric models.
关键词：KeywordsSystem IdentificationSemi-Supervised LearningRegularization