English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive noises. To reduce the influence of the colored noises on parameter estimates, we turn the original model into the new model with white noises by using the model transformation. In consideration of the high dimensions of multivariate systems and different types of parameters, we decompose the transformed system model into several sub-models in accordance with the number of the outputs. However, the decomposition results in many redundant estimates. Here, we propose two algorithms to cut down the redundant estimates. By taking the average of the parameter estimation vectors, we develop a model transformation partially-coupled (MT-PC) recursive least squares algorithm. Moreover, an MT-PC recursive least squares algorithm is presented by means of the coupling identification concept. Compared with the multivariate recursive generalized least squares algorithm, the two algorithms have higher computational efficiencies and smaller estimation errors. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed algorithms.

Partially-Coupled Recursive Least Squares Algorithm for Multivariate Systems Based on the Model Transformation