Decoupling multivariate functions using a non-parametric filtered CPD approach

Jan Decuyper, Koen Tiels, Siep Weiland, Johan Schoukens

Research output: Contribution to journalConference paper

6 Citations (Scopus)


Black-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.

Original languageEnglish
Pages (from-to)451-456
Number of pages6
Issue number7
Publication statusPublished - 1 Jul 2021
Event19th IFAC Symposium on System Identification, SYSID 2021 - Padova, Italy
Duration: 13 Jul 202116 Jul 2021


  • CPD
  • Decoupling multivariate functions
  • Model reduction
  • Nonlinear system identification


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