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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2018

18.08.2016 | Original Article

Novel generalization of Volterra LMS algorithm to fractional order with application to system identification

verfasst von: Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor Raja, Muhammad Saeed Aslam, Naseer Ahmed

Erschienen in: Neural Computing and Applications | Ausgabe 6/2018

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Abstract

In the present study, a novel generalization of Volterra least mean square (V-LMS) algorithm to fractional order is presented by exploiting the renowned strength of fractional adaptive signal processing. The fractional derivative term is introduced in weight adaptation mechanism of standard V-LMS to derive the recursive relations for modified V-LMS (MV-LMS) algorithm. The design scheme of MV-LMS algorithm is applied to parameter identification of Box–Jenkins system by taking different values of fractional orders, step-size variations and small to high signal-to-noise ratios. The proposed adaptive variables of MV-LMS are compared from true parameters of Box–Jenkins systems as well as with the results of the V-LMS for each case. The correctness and reliability of the given scheme MV-LMS are also validated from the results of statistical performance measures calculated on large dataset based on multiple independent runs.
Vorheriger Artikel Using flower pollination algorithm and atomic potential function for shape matching
Nächster Artikel Heat and mass transfer in a micropolar fluid with Newtonian heating: an exact analysis

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Metadaten
Titel
Novel generalization of Volterra LMS algorithm to fractional order with application to system identification
verfasst von
Naveed Ishtiaq Chaudhary
Muhammad Asif Zahoor Raja
Muhammad Saeed Aslam
Naseer Ahmed
Publikationsdatum
18.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2548-5

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