Gradient-based identification methods for hammerstein nonlinear ARMAX models

Nonlinear Dynamics

Volumn 45, Issue 1-2, 2006, Pages 31-43

  Ding, Feng ^a   Shi, Yang ^b   Chen, Tongwen ^c  

^a JIANGNAN UNIVERSITY   (China)

^b UNIVERSITY OF SASKATCHEWAN   (Canada)

^c UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Convergence properties; Hammerstein models; Least squares; Martingale convergence theorem; Parameter estimation; Recursive identification; Stochastic gradient; Wiener models

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; COMPUTER SIMULATION; GRADIENT METHODS; INFORMATION RETRIEVAL; ITERATIVE METHODS; NONLINEAR SYSTEMS; PARAMETER ESTIMATION; VECTORS;

CONVERGENCE PROPERTIES; HAMMERSTEIN MODELS; LEAST SQUARES; MARTINGALE CONVERGENCE THEOREM; RECURSIVE IDENTIFICATION; STOCHASTIC GRADIENTS; WIENER MODELS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 33745915835     PISSN: 0924090X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11071-005-1850-z     Document Type: Article

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