Publications

@conference{Mayoufi20203701b,

title = {An optimal instrumental variable approach for continuous-time multiple input-single output fractional model identification},

author = {Abir Mayoufi and St\'{e}phane Victor and Rachid Malti and Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107518806\&doi=10.1016%2fj.ifacol.2020.12.2055\&partnerID=40\&md5=a45fe2670f212782b1c97c7adcd159c9},

doi = {10.1016/j.ifacol.2020.12.2055},

year  = {2020},

date = {2020-01-01},

journal = {IFAC-PapersOnLine},

volume = {53},

pages = {3701 \textendash 3706},

abstract = {This paper proposes an instrumental variable approach for continuous-time system identification using fractional models with multiple input single output context. This work is an extension of the simplified refined instrumental variable approach (srivcf) developed for single input-single output fractional model identification (Malti et al. (2008a); Victor et al. (2013)) to the multiple input-single output case. Monte Carlo simulation analysis is used to demonstrate the performance of the proposed approach. A study is then provided to motivate differentiation order estimation, and more specifically, commensurate order estimation. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)},

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

keywords = {Continuous time systems, Continuous-time, Fractional model, Fractional model identification, Instrumental variables, Monte Carlo methods, Multiple input single outputs, Order estimation, Refined instrumental variables, Single input single output},

pubstate = {published},

tppubtype = {conference}

}

@conference{Mayoufi20203701,

title = {An optimal instrumental variable approach for continuous-time multiple input-single output fractional model identification},

author = {Abir Mayoufi and St\'{e}phane Victor and Rachid Malti and Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107518806\&doi=10.1016%2fj.ifacol.2020.12.2055\&partnerID=40\&md5=a45fe2670f212782b1c97c7adcd159c9},

doi = {10.1016/j.ifacol.2020.12.2055},

year  = {2020},

date = {2020-01-01},

journal = {IFAC-PapersOnLine},

volume = {53},

pages = {3701 \textendash 3706},

abstract = {This paper proposes an instrumental variable approach for continuous-time system identification using fractional models with multiple input single output context. This work is an extension of the simplified refined instrumental variable approach (srivcf) developed for single input-single output fractional model identification (Malti et al. (2008a); Victor et al. (2013)) to the multiple input-single output case. Monte Carlo simulation analysis is used to demonstrate the performance of the proposed approach. A study is then provided to motivate differentiation order estimation, and more specifically, commensurate order estimation. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)},

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

keywords = {Continuous time systems, Continuous-time, Fractional model, Fractional model identification, Instrumental variables, Monte Carlo methods, Multiple input single outputs, Order estimation, Refined instrumental variables, Single input single output},

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}

}