Recursive Identification of Hammerstein Systems with application to Electrically Stimulated Muscle
Recursive Identification of Hammerstein Systems with application to Electrically Stimulated Muscle
Two methods for recursive identification of Hammerstein systems are considered. In the first method, the recursive least squares algorithm is applied to an overparameterized representation of the Hammerstein model and a rank-1 approximation is used to recover the linear and nonlinear parameters from the estimated overparameterized form. In the second method, the linear and nonlinear parameters are recursively estimated in an alternate manner. The superiority of the second method is confirmed using a numerical simulation example, together with experimentally measured data from electrically stimulated muscles.
386-396
Le, Fengmin
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Markovsky, Ivan
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Freeman, Christopher
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Rogers, Eric
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2012
Le, Fengmin
3e44aa4d-33ea-4697-a9c2-b2bff35cee3c
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Le, Fengmin, Markovsky, Ivan, Freeman, Christopher and Rogers, Eric
(2012)
Recursive Identification of Hammerstein Systems with application to Electrically Stimulated Muscle.
Control Engineering Practice, 20 (4), .
Abstract
Two methods for recursive identification of Hammerstein systems are considered. In the first method, the recursive least squares algorithm is applied to an overparameterized representation of the Hammerstein model and a rank-1 approximation is used to recover the linear and nonlinear parameters from the estimated overparameterized form. In the second method, the linear and nonlinear parameters are recursively estimated in an alternate manner. The superiority of the second method is confirmed using a numerical simulation example, together with experimentally measured data from electrically stimulated muscles.
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Published date: 2012
Organisations:
EEE, Southampton Wireless Group
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Local EPrints ID: 271583
URI: http://eprints.soton.ac.uk/id/eprint/271583
ISSN: 0967-0661
PURE UUID: 1361b2d4-5d69-41d5-aa85-60de9541f899
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Date deposited: 23 Sep 2010 15:24
Last modified: 15 Mar 2024 02:42
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Contributors
Author:
Fengmin Le
Author:
Ivan Markovsky
Author:
Christopher Freeman
Author:
Eric Rogers
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