Nonlinear System Identification Using Varying Exponential Even Mirror Fourier Nonlinear Filters

Patel, V; Pradhan, S

Nonlinear System Identification Using Varying Exponential Even Mirror Fourier Nonlinear Filters

Patel, V Pradhan, S

Permalink

Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Journal of Signal Processing Systems, 2023, 95, (6), pp. 671-678
Issue Date:: 2023-06-01

Closed Access

	Filename	Description	Size
	s11265-023-01871-x.pdf	Accepted version	3.19 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Patel, V
dc.contributor.author	Pradhan, S https://orcid.org/0000-0002-7463-0932
dc.date.accessioned	2024-02-05T23:31:44Z
dc.date.available	2024-02-05T23:31:44Z
dc.date.issued	2023-06-01
dc.identifier.citation	Journal of Signal Processing Systems, 2023, 95, (6), pp. 671-678
dc.identifier.issn	1939-8018
dc.identifier.issn	1939-8115
dc.identifier.uri	http://hdl.handle.net/10453/175326
dc.description.abstract	Adaptive exponential functional link neural network (AeFLNN) based on functional link architecture is a recently added member in the family of linear-in-parameter nonlinear filters. However, AeFLNN does not fulfill the criteria of universal approximate due to the absence of cross-terms in its functional expansion. Therefore, a new nonlinear filter based on even mirror Fourier nonlinear filters (EMFN) and exponentially varying sinusoidal basis functions named varying exponential EMFN (VeEMFN) is presented in this paper. To further improve the modeling accuracy, an independently varying exponential EMFN (IVeEMFN) filter is designed to allow each sinusoid in the basis function to grow or decay independently. A suitable update rule for updating the filter coefficients and exponential parameters are derived with the bounds on the learning rates is also presented. The simulation study demonstrates the enhanced modeling accuracy of the proposed filters.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Journal of Signal Processing Systems
dc.relation.isbasedon	10.1007/s11265-023-01871-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Computer Hardware & Architecture
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4611 Machine learning
dc.title	Nonlinear System Identification Using Varying Exponential Even Mirror Fourier Nonlinear Filters
dc.type	Journal Article
utslib.citation.volume	95
utslib.for	0906 Electrical and Electronic Engineering
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-05T23:31:43Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	95
utslib.citation.issue	6

Abstract:

Adaptive exponential functional link neural network (AeFLNN) based on functional link architecture is a recently added member in the family of linear-in-parameter nonlinear filters. However, AeFLNN does not fulfill the criteria of universal approximate due to the absence of cross-terms in its functional expansion. Therefore, a new nonlinear filter based on even mirror Fourier nonlinear filters (EMFN) and exponentially varying sinusoidal basis functions named varying exponential EMFN (VeEMFN) is presented in this paper. To further improve the modeling accuracy, an independently varying exponential EMFN (IVeEMFN) filter is designed to allow each sinusoid in the basis function to grow or decay independently. A suitable update rule for updating the filter coefficients and exponential parameters are derived with the bounds on the learning rates is also presented. The simulation study demonstrates the enhanced modeling accuracy of the proposed filters.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/175326