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Adaptive strategies for parameter estimation of Box–Jenkins systems

Content Provider	IET Digital Library
Author	Raja, Muhammad Asif Zahoor Chaudhary, Naveed Ishtiaq
Abstract	This study presents a novel application of fractional adaptive algorithms for parameter identification of Box–Jenkins (BJ) systems. The idea is to adapt the unknown parameter vector of the BJ system by the fractional least mean square (FLMS) algorithm for three different values of the fractional order and then to compare the estimated results with state of the art Volterra least mean square and kernel least mean square adaptive algorithms to validate and verify the correctness of the design scheme. The reliability and effectiveness of the proposed scheme is analysed through the results of the statistical analysis based on sufficient large number of independent runs and it is found that the proposed FLMS algorithm provides consistently accurate and convergent results for BJ systems under different scenarios.
Starting Page	968
Ending Page	980
Page Count	13
ISSN	17519675
Volume Number	8
e-ISSN	17519683
Issue Number	Issue 9, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/8/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0438
Journal	IET Signal Processing
Publisher Date	2014-12-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Estimation BJ System Box–Jenkins System Filtering Method in Signal Processing FLMS Algorithm Fractional Adaptive Algorithm Fractional Least Mean Square Algorithm Fractional Order Interpolation And Function Approximation Kernel Least Mean Square Adaptive Algorithm Least Mean Squares Method Nonlinear Filter Numerical Analysis Parameter Estimation Reliability Signal Processing Theory Statistical Analysis Statistics Unknown Parameter Vector Volterra Least Mean Square Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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