Publications

@conference{Yakoub20221003b,

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

author = {Zaineb Yakoub and Messaoud Amairi and Manel Chetoui and Mohamed 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},

year  = {2022},

date = {2022-01-01},

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

pages = {1003 \textendash 1008},

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, Bias compensation, Fractional order, Fractional order differentiation, Fractional-order systems, Identification, Least Square, Least squares approximations, Model problems, Modelling and identifications, Recursive least-squares method, System-identification},

pubstate = {published},

tppubtype = {conference}

}

@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}

}

@conference{Chetoui2014b,

title = {Fourth order cumulants in EIV system identification using fractional models. An electronic application},

author = {Manel Chetoui and Magalie Thomassin and Rachid Malti and Mohamed Aoun and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918524787\&doi=10.1109%2fICFDA.2014.6967436\&partnerID=40\&md5=4864e15da0b3922a4215fa5c5ac8366c},

doi = {10.1109/ICFDA.2014.6967436},

year  = {2014},

date = {2014-01-01},

journal = {2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014},

abstract = {This paper considers the problem of identifying continuous-time fractional systems from noisy input/output measurements. Firstly, the differentiation orders are fixed and the differential equation coefficients are estimated using an estimator based on Higher-Order Statistics: fractional fourth-order cumulants based least squares (ffocls). Then, the commensurate order is estimated along with the differential equation coefficients using a non linear optimization technique combined to the ffocls algorithm (co-ffocls). Under some assumptions on the distributional properties of additive noises and the noise-free input signals, the developed estimators give consistent results. Hence, the noise-free input signal is assumed to be non gaussian, whereas the additive noises are assumed to be gaussian. The performances of the developed algorithms are assessed through a practical application for modeling a real electronic system. © 2014 IEEE.},

note = {Cited by: 0},

keywords = {Additive noise, Additives, commensurate order, Continuous time systems, Continuous-time, Differential equations, Electronic systems, Errors in variables, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Identification (control systems), Nonlinear programming, Religious buildings, Signal processing},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui2014,

title = {Fourth order cumulants in EIV system identification using fractional models. An electronic application},

author = {Manel Chetoui and Magalie Thomassin and Rachid Malti and Mohamed Aoun and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918524787\&doi=10.1109%2fICFDA.2014.6967436\&partnerID=40\&md5=4864e15da0b3922a4215fa5c5ac8366c},

doi = {10.1109/ICFDA.2014.6967436},

year  = {2014},

date = {2014-01-01},

journal = {2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014},

abstract = {This paper considers the problem of identifying continuous-time fractional systems from noisy input/output measurements. Firstly, the differentiation orders are fixed and the differential equation coefficients are estimated using an estimator based on Higher-Order Statistics: fractional fourth-order cumulants based least squares (ffocls). Then, the commensurate order is estimated along with the differential equation coefficients using a non linear optimization technique combined to the ffocls algorithm (co-ffocls). Under some assumptions on the distributional properties of additive noises and the noise-free input signals, the developed estimators give consistent results. Hence, the noise-free input signal is assumed to be non gaussian, whereas the additive noises are assumed to be gaussian. The performances of the developed algorithms are assessed through a practical application for modeling a real electronic system. © 2014 IEEE.},

note = {Cited by: 0},

keywords = {Additive noise, Additives, commensurate order, Continuous time systems, Continuous-time, Differential equations, Electronic systems, Errors in variables, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Identification (control systems), Nonlinear programming, Religious buildings, Signal processing},

pubstate = {published},

tppubtype = {conference}

}