Publications

@conference{Yakoub20221003,

title = {Bias Recursive Least Squares Method for Fractional Order System Identification},

author = {Z. Yakoub and M. Amairi and M. Chetoui and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143798506\&doi=10.1109%2fSSD54932.2022.9955869\&partnerID=40\&md5=33815d9cbb662e9c28d357625582a869},

doi = {10.1109/SSD54932.2022.9955869},

isbn = {9781665471084},

year  = {2022},

date = {2022-01-01},

journal = {2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022},

pages = {1003-1008},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This paper mainly studies the modeling and identification problems for fractional order systems. A novel modeling scheme based on an online identification technique is investigated. Firstly, the recursive least squares algorithm is applied to identify the fractional order system. However, if the measurement of the output signal is affected by an additive noise this algorithm is unable to give consistent estimates. Thus, this contribution implements a technique based on the bias compensation principle. The main idea is to eliminate the introduced bias by adding a correction term in the recursive least squares estimates. The results of the simulated example indicate that the proposed estimator provides good accuracy. © 2022 IEEE.},

note = {cited By 0},

keywords = {Additive noise, Algebra; Least squares approximations, Bias compensation; Fractional order; Fractional order differentiation; Fractional-order systems; Identification; Least Square; Model problems; Modelling and identifications; Recursive least-squares method; System-identification},

pubstate = {published},

tppubtype = {conference}

}