Identification of Errors-in-Variable System With Heteroscedastic Noise and Partially Known Input Using Variational Bayesian

In this article, an approach for identification of an errors-in-variable system whose output is contaminated by heteroscedastic noise is developed. A Markov chain is applied to depict the correlation of the switching of heteroscedastic noise model. The estimation of model parameters adopts a variational Bayesian algorithm. The advantage of the Bayesian approach is the full probability description of the estimates while the classical expectation-maximization algorithm only provides point estimation. A simulated numerical example and an experimental study on a polyester fiber process are provided to demonstrate the effectiveness of the proposed method. Three performance indexes, normalized mean-absolute error, mean-relative error and root-mean-squared error, are used to evaluate the performance of the proposed algorithm. Meanwhile, Monte Carlo cross validations are performed to demonstrate the effectiveness and superiority of the proposed algorithm.

Automated summary

In this article, a method is proposed for identifying an errors-in-variable system contaminated by heteroscedastic noise. A Markov chain is used to describe the correlation of the switching of the heteroscedastic noise model. A variational Bayesian algorithm is employed for estimating the model parameters. The effectiveness of the proposed method is demonstrated through simulated numerical examples and an experimental study on a polyester fiber process. Three performance indexes are used to evaluate the algorithm's performance and Monte Carlo cross validations are performed to demonstrate its effectiveness and superiority.