Weighted hierarchical stochastic gradient identification algorithms for ARX models

DOI10.1080/00207721.2020.1829163zbMath1483.93672OpenAlexW3094070695MaRDI QIDQ5027992

Ying Zhang, Ruiqi Dong, Ai-guo Wu

Publication date: 8 February 2022

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2020.1829163

zbMATH Keywords

convergence analysissystem identificationparameter identification

Mathematics Subject Classification ID

Linear systems in control theory (93C05) Identification in stochastic control theory (93E12)

Related Items

Fractional-based stochastic gradient algorithms for time-delayed ARX models ⋮ Bias-compensated least squares and fuzzy PSO based hierarchical identification of errors-in-variables Wiener systems ⋮ Momentum‐innovation recursive least squares identification algorithm for a servo turntable system based on the output error model

Cites Work

• Unnamed Item
• Unnamed Item
• A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems
• Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling
• Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems
• Signal frequency and parameter estimation for power systems using the hierarchical identification principle
• Hierarchical least squares algorithms for single-input multiple-output systems based on the auxiliary model
• Performance analysis of multi-innovation gradient type identification methods
• Some further results on blind identification of MIMO FIR channels via second-order statistics
• Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model
• Modified multi-innovation stochastic gradient algorithm for Wiener-Hammerstein systems with backlash
• Hierarchical gradient-based identification of multivariable discrete-time systems
• Hierarchical stochastic gradient algorithm and its performance analysis for a class of bilinear-in-parameter systems
• Recursive Identification and Adaptive Prediction in Linear Stochastic Systems
• Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses
• Hierarchical parameter and state estimation for bilinear systems
• The innovation algorithms for multivariable state‐space models
• Convergence Analysis of Weighted Stochastic Gradient Identification Algorithms Based on Latest‐Estimation for ARX Models