文章基本信息

标题：Surrogate Data and Nonlinear Distortion for Nonlinear Model Development—A Preliminary Study
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作者：Matias Waller
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：503-508
DOI：10.1016/j.ifacol.2018.09.195
语种：English
出版社：Elsevier
摘要：AbstractSome possibilities for how data-based nonlinearity tests can support the development of nonlinear dynamical models are explored. Two data-based nonlinearity tests are considered: the use of Fourier-based surrogate data and the method for estimating nonlinear distortion in the frequency domain. Two simulated discrete-time examples and a continuous gray-box model of a laboratory-scale heating process are used to illustrate and discuss how these methods can support nonlinear model development.
关键词：KeywordsNonlinear systemsnonlinear distortionidentificationstatistical analysisfrequency-response characteristicsheat exchangers