Partially‐coupled gradient‐based iterative algorithms for multivariable output‐error‐like systems with autoregressive moving average noises
Hao Ma, Xiao Zhang, Qinyao Liu, Feng Ding, Xuebo Jin, Ahmed Alsaedi, Tasawar Hayat
Abstract
The parameter estimation problem of multivariable output‐error‐like systems with autoregressive moving average noises is investigated in this study, and the primary system is segregated into some subsystems and a subsystem generalised extended gradient‐based iterative algorithm is presented according to the decomposition technique. Nevertheless, there exists the common parameter vector in each subsystem, which increases the calculation. By taking the mean value of the common parameter estimation vectors of the subsystems as the optimal estimate of the current iteration, and substituting it into the next iteration, a partially coupled subsystem generalised extended gradient‐based iterative algorithm is proposed. Furthermore, in the cause of further deepening the coupled relationships between the common parameter estimation vectors of two subsystems and to reduce the computational cost and the redundant estimates, a partially coupled generalised extended gradient‐based iterative algorithm is presented by making use of the coupling identification concept. Finally, the simulation results show that the coupled gradient‐based iterative algorithms are effective.