Publications

@conference{BenMoussa2021942,

title = {MISO fractional systems identification with fractional models in the EIV context},

author = {N. Ben Moussa and M. Chetoui and M. Amairi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107483317\&doi=10.1109%2fSSD52085.2021.9429412\&partnerID=40\&md5=80f0cb851146ac0dffd525cbe8fbe408},

doi = {10.1109/SSD52085.2021.9429412},

isbn = {9781665414937},

year  = {2021},

date = {2021-01-01},

journal = {18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021},

pages = {942-947},

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

abstract = {This paper proposes a new multi-input-single-output (MISO) system identification methods with fractional models in the errors-in-variables context. The developed methods are based on the instrumental variables and use the Higher-Order Statistics (HOS), such as the third-order cumulants, to obtain an unbiased estimate. Two different cases are established : The first supposes that the fractional orders of the single input-single-output (SISO) systems decomposing the MISO system are known a priori and only their linear coefficients are estimated. In the second case, the fractional orders are optimized along with linear coefficients. A Monte Carlo simulations are used, in a numerical example, to analyze the consistency of the developed estimators. © 2021 IEEE.},

note = {cited By 1},

keywords = {Errors in variables; Fractional systems; Higherorder statistic (HOS); Instrumental variables; Linear coefficients; Multi-input-single-output systems; Single input single output systems; Unbiased estimates, Higher order statistics, Image segmentation; Monte Carlo methods},

pubstate = {published},

tppubtype = {conference}

}

@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 = {M. Chetoui and M. 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},

isbn = {9781728118208},

year  = {2019},

date = {2019-01-01},

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

pages = {90-95},

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

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 6},

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

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}

}

@conference{Chetoui20121641b,

title = {EIV methods for system identification with fractional models},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867084248\&doi=10.3182%2f20120711-3-BE-2027.00270\&partnerID=40\&md5=dada6960d43c8fb326f09161d504d824},

doi = {10.3182/20120711-3-BE-2027.00270},

year  = {2012},

date = {2012-01-01},

journal = {IFAC Proceedings Volumes (IFAC-PapersOnline)},

volume = {16},

number = {PART 1},

pages = {1641 \textendash 1646},

abstract = {This paper deals with continuous-time system identification with fractional models in Errors-In-Variables context. Two estimators based on Higher-Order Statistics (third-order cumulants) are proposed. A State Variable Filter approach is extended to fractional orders to compute fractional derivatives of third-order cumulants estimates. The performance of the proposed algorithms is illustrated in a numerical example. Firstly, differentiation orders are fixed and differential equation coefficients are estimated. The consistency of the proposed estimators is evaluated through a study of the tuning parameter and Monte Carlo simulations. Then, the commensurate differentiation order is optimized along with the differential equation coefficients. © 2012 IFAC.},

note = {Cited by: 14},

keywords = {Continuous time systems, Continuous-time, Cumulants, Differential equations, Errors in variables, Fractional SVF, Higher order statistics, Identification (control systems), Iterative, Iterative methods, Least Square, Monte Carlo methods, Religious buildings},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui20121641,

title = {EIV methods for system identification with fractional models},

author = {M. Chetoui and R. Malti and M. Thomassin and M. Aoun and S. Najar and A. Oustaloup and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867084248\&doi=10.3182%2f20120711-3-BE-2027.00270\&partnerID=40\&md5=dada6960d43c8fb326f09161d504d824},

doi = {10.3182/20120711-3-BE-2027.00270},

issn = {14746670},

year  = {2012},

date = {2012-01-01},

journal = {IFAC Proceedings Volumes (IFAC-PapersOnline)},

volume = {16},

number = {PART 1},

pages = {1641-1646},

publisher = {IFAC Secretariat},

abstract = {This paper deals with continuous-time system identification with fractional models in Errors-In-Variables context. Two estimators based on Higher-Order Statistics (third-order cumulants) are proposed. A State Variable Filter approach is extended to fractional orders to compute fractional derivatives of third-order cumulants estimates. The performance of the proposed algorithms is illustrated in a numerical example. Firstly, differentiation orders are fixed and differential equation coefficients are estimated. The consistency of the proposed estimators is evaluated through a study of the tuning parameter and Monte Carlo simulations. Then, the commensurate differentiation order is optimized along with the differential equation coefficients. © 2012 IFAC.},

note = {cited By 14},

keywords = {Continuous time systems; Differential equations; Identification (control systems); Iterative methods; Monte Carlo methods; Religious buildings, Continuous-time; Cumulants; Errors in variables; Fractional SVF; Iterative; Least Square, Higher order statistics},

pubstate = {published},

tppubtype = {conference}

}