Latest-Estimation Based Hierarchical Recursive Extended Least Squares algorithm for ARMAX model

Ai-Guo Wu, Shiwei Liu, Rui-Qi Dong

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    For ARMAX models, a Latest-Estimation Based Hierarchical Recursive Extended Least Squares algorithm is presented in this paper. The basic idea is to make full use of the latest estimation, and combine this with the hierarchical idea. In the proposed algorithm, the estimates of the white noise information vector is updated by using the latest estimation. The convergence performance of the proposed LE-HRELS algorithm is simply analyzed. It is shown by a numerical example that the LE-HRELS algorithm possesses faster convergence speed and higher convergence precision compared with the standard RELS and the HRELS algorithm.
    Original languageEnglish
    Title of host publication2016 35th Chinese Control Conference (CCC)
    Publication statusPublished - 27 Jul 2016

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