Identification of fractional Hammerstein model for electrical stimulated muscle: An application of fuzzy-weighted differential evolution

Mehmood, A; Raja, MAZ; Jalili, M; Ho Ling, S

Identification of fractional Hammerstein model for electrical stimulated muscle: An application of fuzzy-weighted differential evolution

Mehmood, A Raja, MAZ Jalili, M Ho Ling, S

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Biomedical Signal Processing and Control, 2024, 87
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Mehmood, A
dc.contributor.author	Raja, MAZ
dc.contributor.author	Jalili, M
dc.contributor.author	Ho Ling, S
dc.date.accessioned	2024-02-05T23:40:44Z
dc.date.available	2024-02-05T23:40:44Z
dc.date.issued	2024-01-01
dc.identifier.citation	Biomedical Signal Processing and Control, 2024, 87
dc.identifier.issn	1746-8094
dc.identifier.issn	1746-8108
dc.identifier.uri	http://hdl.handle.net/10453/175328
dc.description.abstract	Fractional order representations model complex systems with less parameters, improved accuracy and enhanced robustness in control systems but the system identification of fractional order systems is highly complicated and challenging task because of substantial nonlinearity of the input, unknown linear/nonlinear parameters of the blocks, and unknown fractional order. In this paper, parameter estimation technique is proposed based on fuzzy-weighted differential evolution algorithm for fractional electrically stimulated muscle models, which is generalization of fractional Hammerstein controlled autoregressive model vital for rehabilitation of spinal cord injury (SCI) patients. The system identification problem of fractional electrically stimulated muscle models (FESMMs) is formulated via mean square error approximation between the true and estimated response of FESMMs. The parameters of FESMMs are identified by employing fuzzy weighted differential evolution algorithm optimization knacks of with polynomial, cubic spline and sigmoidal input nonlinearities on various noise variances in the system dynamics. Comparison of results from actual to calculated responses indicates closeness up to 4 decimal places of accuracy for FESMM with polynomial type nonlinearity, up to 6 decimal places for FESMM with sigmoidal type kernel, and up to 5 decimal for FESMM with cubic spline type nonlinearity for different low and high signal to noise scenarios i.e., σ2=0.0022,0.022,0.22, which verify the precision, efficacy, stability and reliability of the fuzzy weighted differential evolution algorithm for system identification of FESMMs in rehabilitation scenarios of SCI.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Biomedical Signal Processing and Control
dc.relation.isbasedon	10.1016/j.bspc.2023.105545
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, 1004 Medical Biotechnology
dc.subject.classification	Biomedical Engineering
dc.subject.classification	3006 Food sciences
dc.subject.classification	4003 Biomedical engineering
dc.title	Identification of fractional Hammerstein model for electrical stimulated muscle: An application of fuzzy-weighted differential evolution
dc.type	Journal Article
utslib.citation.volume	87
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1004 Medical Biotechnology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2026-01-01T00:00:00+1000Z
dc.date.updated	2024-02-05T23:40:43Z
pubs.publication-status	Published
pubs.volume	87

Abstract:

Fractional order representations model complex systems with less parameters, improved accuracy and enhanced robustness in control systems but the system identification of fractional order systems is highly complicated and challenging task because of substantial nonlinearity of the input, unknown linear/nonlinear parameters of the blocks, and unknown fractional order. In this paper, parameter estimation technique is proposed based on fuzzy-weighted differential evolution algorithm for fractional electrically stimulated muscle models, which is generalization of fractional Hammerstein controlled autoregressive model vital for rehabilitation of spinal cord injury (SCI) patients. The system identification problem of fractional electrically stimulated muscle models (FESMMs) is formulated via mean square error approximation between the true and estimated response of FESMMs. The parameters of FESMMs are identified by employing fuzzy weighted differential evolution algorithm optimization knacks of with polynomial, cubic spline and sigmoidal input nonlinearities on various noise variances in the system dynamics. Comparison of results from actual to calculated responses indicates closeness up to 4 decimal places of accuracy for FESMM with polynomial type nonlinearity, up to 6 decimal places for FESMM with sigmoidal type kernel, and up to 5 decimal for FESMM with cubic spline type nonlinearity for different low and high signal to noise scenarios i.e., σ2=0.0022,0.022,0.22, which verify the precision, efficacy, stability and reliability of the fuzzy weighted differential evolution algorithm for system identification of FESMMs in rehabilitation scenarios of SCI.

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http://hdl.handle.net/10453/175328