文章基本信息

标题：Decoupling multivariate functions using a non-parametric Filtered CPD approach ⁎
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作者：Jan Decuyper ; Koen Tiels ; Siep Weiland 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：451-456
DOI：10.1016/j.ifacol.2021.08.401
语种：English
出版社：Elsevier
摘要：AbstractBlack-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is a pragmatic and often necessary step considering the black-box nature of the problem. However, having identified a suitable function, there is no need to stick to the original basis. So-called decoupling techniques aim at translating multivariate functions into an alternative basis, thereby both reducing the number of parameters and retrieving underlying structure. In this work a filtered canonical polyadic decomposition (CPD) is introduced. It is a non-parametric method which is able to retrieve decoupled functions even when facing non-unique decompositions. Tackling this obstacle paves the way for a large number of modelling applications.
关键词：Keywordsdecoupling multivariate functionsCPDmodel reductionnonlinear system identification