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Decoupling multivariate functions using a non-parametric Filtered CPD approach
- Source :
- IFAC-PapersOnLine. 54:451-456
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
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.<br />Accepted for presentation at the 19th IFAC Symposium on System Identification (SYSID 2021)
- Subjects :
- Multivariate statistics
Nonlinear system identification
Basis (linear algebra)
Model reduction
Computer science
Nonparametric statistics
Basis function
Systems and Control (eess.SY)
CPD
Decoupling (cosmology)
Function (mathematics)
Electrical Engineering and Systems Science - Systems and Control
Polynomial basis
Control and Systems Engineering
FOS: Electrical engineering, electronic engineering, information engineering
Decoupling multivariate functions
Algorithm
Subjects
Details
- ISSN :
- 24058963
- Volume :
- 54
- Database :
- OpenAIRE
- Journal :
- IFAC-PapersOnLine
- Accession number :
- edsair.doi.dedup.....da0741b0b73a00de84ed7f91dc544a82