Decoupling multivariate functions using a non-parametric Filtered CPD approach⁎This work was supported by the Flemish fund for scientific research FWO under license number G0068.18N. Issue 7 (2021)

• Record Type: Journal Article
• Authors: Decuyper, Jan; Tiels, Koen; Weiland, Siep; Schoukens, Johan
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 7(2021)
• Page Start: 451
• Page End: 456
• Publication Date: 2021
• Subjects: decoupling multivariate functions -- CPD -- model reduction -- nonlinear system identification; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.401
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.