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.
ORCID iDs
Liu, Qinyao, Ding, Feng
ORCID: https://orcid.org/0000-0002-9787-4171 and Yang, Erfu ORCID: https://orcid.org/0000-0003-1813-5950;
Item type: Article ID code: 72121 Dates: DateEvent31 December 2018Published25 September 2018Published Online16 September 2018AcceptedKeywords: 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 ManagementDepositing user: Pure Administrator Date deposited: 21 Apr 2020 13:29 Last modified: 03 Apr 2021 00:42 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/72121
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