文章基本信息

标题：Learning nonlinearities in the decoupling problem with structured CPD
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作者：Yassine Zniyed ; Konstantin Usevich ; Sebastian Miron 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：685-690
DOI：10.1016/j.ifacol.2021.08.440
语种：English
出版社：Elsevier
摘要：AbstractDecoupling of multivariate functions is an important problem in block-structured system identification. In the literature, different tensor-based solutions have been proposed to solve this problem using a canonical polyadic decomposition (CPD) of a Jacobian tensor. In a recent work, it has been proposed to add polynomial constraint on the model, which leads to a constrained CPD model with two factors depending nonlinearly on each other. In this work, we are interested in the estimation of the static nonlinearities with polynomial constraints. Using the reformulation as a structured CPD, we propose an alternating least squares algorithm with constrained rank-one terms. Numerical simulations show the performance and the usefulness of the proposed method compared to a competing approach.
关键词：Keywordspolynomial decouplingtensor decompositioncanonical polyadic decompositionstructured tensors