Publications

@article{Yakoub2022e,

title = {A Bias-Corrected Method for Fractional Linear Parameter Varying Systems},

author = {Zaineb Yakoub and Omar Naifar and Messaoud Amairi and Manel Chetoui and Mohamed Aoun and Abdellatif Ben Makhlouf},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128182629\&doi=10.1155%2f2022%2f7278157\&partnerID=40\&md5=6ea775beb03cf2de78d054b3e4819d41},

doi = {10.1155/2022/7278157},

year  = {2022},

date = {2022-01-01},

journal = {Mathematical Problems in Engineering},

volume = {2022},

abstract = {This paper proposes an identification algorithm for the fractional Linear Parameter Varying (LPV) system considering noisy scheduling and output measurements. A bias correction technique is provided in order to compensate for the bias caused by the least squares algorithm. This approach was created to estimate either coefficients or fractional-order differentiation, and it has been proven to produce unbiased and reliable results. The suggested method's performance is assessed by the identification of two fractional models and was compared with Nelder-Mead Simplex method. © 2022 Zaineb Yakoub et al.},

note = {Cited by: 1; All Open Access, Gold Open Access},

keywords = {Bias correction, Correction techniques, Fractional model, Fractional order, Identification algorithms, LeastSquare algorithm, Linear parameter varying systems, Linear programming, Linear systems, Nelder-Mead simplex methods, Performance, Reliable results},

pubstate = {published},

tppubtype = {article}

}

@article{Yakoub2022,

title = {A Bias-Corrected Method for Fractional Linear Parameter Varying Systems},

author = {Z. Yakoub and O. Naifar and M. Amairi and M. Chetoui and M. Aoun and A. B. Makhlouf},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128182629\&doi=10.1155%2f2022%2f7278157\&partnerID=40\&md5=6ea775beb03cf2de78d054b3e4819d41},

doi = {10.1155/2022/7278157},

issn = {1024123X},

year  = {2022},

date = {2022-01-01},

journal = {Mathematical Problems in Engineering},

volume = {2022},

publisher = {Hindawi Limited},

abstract = {This paper proposes an identification algorithm for the fractional Linear Parameter Varying (LPV) system considering noisy scheduling and output measurements. A bias correction technique is provided in order to compensate for the bias caused by the least squares algorithm. This approach was created to estimate either coefficients or fractional-order differentiation, and it has been proven to produce unbiased and reliable results. The suggested method's performance is assessed by the identification of two fractional models and was compared with Nelder-Mead Simplex method. © 2022 Zaineb Yakoub et al.},

note = {cited By 1},

keywords = {Bias correction; Correction techniques; Fractional model; Fractional order; Identification algorithms; LeastSquare algorithm; Linear parameter varying systems; Nelder-Mead simplex methods; Performance; Reliable results, Linear programming, Linear systems},

pubstate = {published},

tppubtype = {article}

}