Recursive Identification of Hammerstein Systems: Convergence Rate and Asymptotic Normality

DOI10.1109/TAC.2016.2629668zbMath1370.93296OpenAlexW2553784559MaRDI QIDQ5358611

George Yin, Bi-Qiang Mu, Chen, Hanfu, Wei Xing Zheng, L. Y. Wang

Publication date: 21 September 2017

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tac.2016.2629668

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Identification in stochastic control theory (93E12) Stochastic approximation (62L20)

Related Items (5)

Robust hierarchical identification of Wiener systems in the presence of dynamic disturbances ⋮ Modified Kalman filtering based multi-step-length gradient iterative algorithm for ARX models with random missing outputs ⋮ Reinforced adaptive parameter estimation with prescribed transient convergence performance ⋮ A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise ⋮ Fully parametric identification for continuous time fractional order Hammerstein systems

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