The residual-based ESG algorithm and its performance analysis

DOI10.1016/j.jfranklin.2009.05.008zbMath1185.93141OpenAlexW2029319979MaRDI QIDQ964326

Feng Ding, Yongsong Xiao, Dong-Qing Wang

Publication date: 15 April 2010

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2009.05.008

zbMATH Keywords

recursive identificationparameter estimationstochastic approximationconvergence propertiesstochastic gradient

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12)

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Cites Work

• Unnamed Item
• Performance analysis of multi-innovation gradient type identification methods
• Optimal controller for uncertain stochastic polynomial systems
• Self-tuning control based on multi-innovation stochastic gradient parameter estimation
• The residual based extended least squares identification method for dual-rate systems
• On consistency of recursive least squares identification algorithms for controlled auto-regression models
• Hierarchical gradient-based identification of multivariable discrete-time systems
• Optimal LQG controller for linear stochastic systems with unknown parameters
• Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
• Gradient-based identification methods for Hammerstein nonlinear ARMAX models
• Convergence analysis of estimation algorithms for dual-rate stochastic systems
• Application of kernel-based stochastic gradient algorithms to option pricing
• Stochastic adaptive prediction and model reference control
• Stochastic approximation and user adaptation in a competitive resource sharing system
• Rate of Convergence for Constrained Stochastic Approximation Algorithms
• General results on the convergence of stochastic algorithms
• Parameter estimation of dual-rate stochastic systems by using an output error method
• Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems