Partially‐coupled least squares based iterative parameter estimation for multi‐variable output‐error‐like autoregressive moving average systems

By Hao Ma, Jianqiang Pan, Feng Ding, Ling Xu, Wenfang Ding. IET Control Theory & Applications, 2019

207 citations recorded by the source corpus.

This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.

Subfields: Computational Mechanics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c7d1f5d24743be9fe6c381d4c65d83a7ddc488d3

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In Control and Systems Engineering

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Citations in the corpus

• Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation (2021, methodology): “Calculate Δl ŵl(k) according to (54), form the information vectors ?̂?1,l(k), ?̂?2,l(k), ?̂?l(k), and F̂l(k) using (52), (53), (51), and (50).”