Publications

@conference{Chetoui201990b,

title = {Instrumental variables based methods for linear systems identification with fractional models in the EIV context},

author = {Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075640184\&doi=10.1109%2fSSD.2019.8893265\&partnerID=40\&md5=2f59e71847ca98f45a7d71b9b46a627d},

doi = {10.1109/SSD.2019.8893265},

year  = {2019},

date = {2019-01-01},

journal = {16th International Multi-Conference on Systems, Signals and Devices, SSD 2019},

pages = {90 \textendash 95},

abstract = {In this paper a new instrumental variables methods based on the Higher-Order-Statistics (fourth order cumulants) are developed for continuous-time system identification with fractional models in the errors in variables context. The fractional orders are supposed known a priori and only the linear coefficients are estimated. The developed algorithms are compared to a fractional fourth order cumulants based least squares algorithm. Their performances are tested through a numerical example in two cases: white and colored noises affecting the input and the output measurements. © 2019 IEEE.},

note = {Cited by: 5},

keywords = {Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui201990,

title = {Instrumental variables based methods for linear systems identification with fractional models in the EIV context},

author = {Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075640184\&doi=10.1109%2fSSD.2019.8893265\&partnerID=40\&md5=2f59e71847ca98f45a7d71b9b46a627d},

doi = {10.1109/SSD.2019.8893265},

year  = {2019},

date = {2019-01-01},

journal = {16th International Multi-Conference on Systems, Signals and Devices, SSD 2019},

pages = {90 \textendash 95},

abstract = {In this paper a new instrumental variables methods based on the Higher-Order-Statistics (fourth order cumulants) are developed for continuous-time system identification with fractional models in the errors in variables context. The fractional orders are supposed known a priori and only the linear coefficients are estimated. The developed algorithms are compared to a fractional fourth order cumulants based least squares algorithm. Their performances are tested through a numerical example in two cases: white and colored noises affecting the input and the output measurements. © 2019 IEEE.},

note = {Cited by: 5},

keywords = {Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters},

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}

}

@conference{Chetoui2013c,

title = {Fourth-order cumulants based method for continuous-time EIV fractional model identification},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883101214\&doi=10.1109%2fSSD.2013.6564119\&partnerID=40\&md5=ea605860656fc371483bf9eefe2534d0},

doi = {10.1109/SSD.2013.6564119},

year  = {2013},

date = {2013-01-01},

journal = {2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013},

abstract = {The errors-in-variables (EIV) system identification problem concerns the dynamic systems whose discrete input and output are corrupted by additive noises, that can be white, colored and/or mutually correlated. In this paper, a new estimator based on Higher-Order Statistics (fourth-order cumulants) is proposed for continuous-time system identification with fractional models. Under some assumptions on the distributional properties of the noise and noise-free signals, the fractional fourth-order cumulants based least squares (ffocls) estimator gives consistent results. A numerical example illustrates the performance of the proposed method. © 2013 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time systems, Continuous-time, Continuous-time system identification, Distributional property, Errors, Errors in variables, Fourth-order cumulants, Fractional differentiation, Fractional model identification, Higher order statistics, Identification (control systems), System identification problems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui2013,

title = {Fourth-order cumulants based method for continuous-time EIV fractional model identification},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883101214\&doi=10.1109%2fSSD.2013.6564119\&partnerID=40\&md5=ea605860656fc371483bf9eefe2534d0},

doi = {10.1109/SSD.2013.6564119},

year  = {2013},

date = {2013-01-01},

journal = {2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013},

abstract = {The errors-in-variables (EIV) system identification problem concerns the dynamic systems whose discrete input and output are corrupted by additive noises, that can be white, colored and/or mutually correlated. In this paper, a new estimator based on Higher-Order Statistics (fourth-order cumulants) is proposed for continuous-time system identification with fractional models. Under some assumptions on the distributional properties of the noise and noise-free signals, the fractional fourth-order cumulants based least squares (ffocls) estimator gives consistent results. A numerical example illustrates the performance of the proposed method. © 2013 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time systems, Continuous-time, Continuous-time system identification, Distributional property, Errors, Errors in variables, Fourth-order cumulants, Fractional differentiation, Fractional model identification, Higher order statistics, Identification (control systems), System identification problems},

pubstate = {published},

tppubtype = {conference}

}