Gradient based and least-squares based iterative identification methods for OE and OEMA systems
Digital Signal Processing
Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems
Computers & Mathematics with Applications
Auxiliary model-based RELS and MI-ELS algorithm for Hammerstein OEMA systems
Computers & Mathematics with Applications
Matrix equations over (R,S)-symmetric and (R,S)-skew symmetric matrices
Computers & Mathematics with Applications
Input--output data filtering based recursive least squares identification for CARARMA systems
Digital Signal Processing
Gradient-based iterative parameter estimation for Box-Jenkins systems
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Parameter estimation with scarce measurements
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Computers & Mathematics with Applications
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Parameter estimation for nonlinear dynamical adjustment models
Mathematical and Computer Modelling: An International Journal
Information Sciences: an International Journal
Nonlinear spline adaptive filtering
Signal Processing
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According to the iterative identification technique and the hierarchical identification principle, this paper presents a two-stage gradient based and a least squares based iterative parameter estimation algorithms (i.e., the hierarchical gradient based iterative algorithm and the hierarchical least squares based iterative algorithm) for controlled autoregressive autoregressive moving average systems. The proposed two-stage least squares based iterative algorithm requires less computation compared with the least squares based iterative algorithm. The simulation results indicate that the two-stage least squares based iterative algorithm converges faster than the two-stage gradient based iterative algorithm.