Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

Liu, Qinyao and Ding, Feng and Yang, Erfu (2018) Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems. Digital Signal Processing: A Review Journal, 83. pp. 323-331. ISSN 1051-2004

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    Abstract

    This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.

    Creators(s): Liu, Qinyao, Ding, Feng ORCID logoORCID: https://orcid.org/0000-0002-9787-4171 and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
    Item type:Article
    ID code:72121
    Keywords:auxiliary model, coupling identification, multivariable system, parameter estimation, stochastic gradient, Electrical engineering. Electronics Nuclear engineering, Signal Processing, Computer Vision and Pattern Recognition, Statistics, Probability and Uncertainty, Computational Theory and Mathematics, Electrical and Electronic Engineering, Artificial Intelligence, Applied Mathematics
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:21 Apr 2020 13:29
    Last modified:09 Oct 2020 03:57
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/72121
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