On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification
Abstract
Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error method. For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping. In this paper, we report some new results on the embeddings/inverse embeddings of the QMIE method. Firstly, we present a general result on the frequency domain inverse embedding (FDIE) that is to find the inverse of the quadratic mapping described by the discrete-time Fourier transform. Then we show the relation between the TDIE and the FDIE from a graph signal processing perspective. Finally, motivated by this perspective, we further propose a graph induced embedding and its inverse, which include the previously introduced embeddings as special cases. This deepens the understanding of input design from a new viewpoint beyond the real domain and the frequency domain viewpoints.
Cite
@article{arxiv.2209.13152,
title = {On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification},
author = {Biqiang Mu and Tianshi Chen and He Kong and Bo Jiang and Lei Wang and Junfeng Wu},
journal= {arXiv preprint arXiv:2209.13152},
year = {2022}
}