학술논문
Variational Bayesian Inference for Robust Identification of PWARX Systems With Time-Varying Time-Delays
Document Type
Periodical
Source
IEEE Transactions on Cybernetics IEEE Trans. Cybern. Cybernetics, IEEE Transactions on. 53(6):3613-3623 Jun, 2023
Subject
Language
ISSN
2168-2267
2168-2275
2168-2275
Abstract
This article presents a robust variational Bayesian (VB) algorithm for identifying piecewise autoregressive exogenous (PWARX) systems with time-varying time-delays. To alleviate the adverse effects caused by outliers, the probability distribution of noise is taken to follow a $t$ -distribution. Meanwhile, a solution strategy for more accurately classifying undecidable data points is proposed, and the hyperplanes used to split data are determined by a support vector machine (SVM). In addition, maximum-likelihood estimation (MLE) is adopted to re-estimate the unknown parameters through the classification results. The time-delay is regarded as a hidden variable and identified through the VB algorithm. The effectiveness of the proposed algorithm is illustrated by two simulation examples.