Weighted tensor decomposition for approximate decoupling of multivariate polynomials
For practitioners in system identification and related fields, this provides a more robust decoupling method for noisy multivariate polynomial data.
The paper generalizes an existing tensor-based method for decoupling multivariate polynomials to handle noisy data by introducing a weight factor, and demonstrates reduced model errors in system identification.
Multivariate polynomials arise in many different disciplines. Representing such a polynomial as a vector of univariate polynomials can offer useful insight, as well as more intuitive understanding. For this, techniques based on tensor methods are known, but these have only been studied in the exact case. In this paper, we generalize an existing method to the noisy case, by introducing a weight factor in the tensor decomposition. Finally, we apply the proposed weighted decoupling algorithm in the domain of system identification, and observe smaller model errors.