2019 |
Chetoui, Manel; Aoun, Mohamed 2019, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters @conference{Chetoui201990b,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. |
Chetoui, Manel; Aoun, Mohamed 2019, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters @conference{Chetoui201990,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. |
2014 |
Chetoui, Manel; Thomassin, Magalie; Malti, Rachid; Aoun, Mohamed; Abdelkrim, Mohamed Naceur 2014, (Cited by: 0). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Chetoui2014b,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. |
Chetoui, Manel; Thomassin, Magalie; Malti, Rachid; Aoun, Mohamed; Abdelkrim, Mohamed Naceur 2014, (Cited by: 0). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Chetoui2014,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. |
2013 |
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Najar, Slaheddine; Aoun, Mohamed; Abdelkrim, Mohamed Naceur; Oustaloup, Alain Fourth-order cumulants based method for continuous-time EIV fractional model identification Conférence 2013, (Cited by: 4). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Chetoui2013c,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. |
Chetoui, Manel; Thomassin, Magalie; Malti, Rachid; Aoun, Mohamed; Najar, Slaheddine; Abdelkrim, Mohamed Naceur; Oustaloup, Alain New consistent methods for order and coefficient estimation of continuous-time errors-in-variables fractional models Article de journal Dans: Computers and Mathematics with Applications, vol. 66, no. 5, p. 860 – 872, 2013, (Cited by: 30; All Open Access, Bronze Open Access). Résumé | Liens | BibTeX | Étiquettes: Algorithms, commensurate order, Differential equations, Errors, Errors in variables, Estimation, Fractional differentiation, Higher order statistics, Identification (control systems), Identification problem, Iterative least squares, Least squares algorithm, Non-linear optimization algorithms, Third-order cumulant @article{Chetoui2013860b,The errors-in-variables identification problem concerns dynamic systems in which input and output signals are contaminated by an additive noise. Several estimation methods have been proposed for identifying dynamic errors-in-variables rational models. This paper presents new consistent methods for order and coefficient estimation of continuous-time systems by errors-in-variables fractional models. First, differentiation orders are assumed to be known and only differential equation coefficients are estimated. Two estimators based on Higher-Order Statistics (third-order cumulants) are developed: the fractional third-order based least squares algorithm (ftocls) and the fractional third-order based iterative least squares algorithm (ftocils). Then, they are extended, using a nonlinear optimization algorithm, to estimate both the differential equation coefficients and the commensurate order. The performances of the proposed algorithms are illustrated with a numerical example. |
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Najar, Slaheddine; Aoun, Mohamed; Abdelkrim, Mohamed Naceur; Oustaloup, Alain Fourth-order cumulants based method for continuous-time EIV fractional model identification Conférence 2013, (Cited by: 4). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Chetoui2013,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. |
Chetoui, Manel; Thomassin, Magalie; Malti, Rachid; Aoun, Mohamed; Najar, Slaheddine; Abdelkrim, Mohamed Naceur; Oustaloup, Alain New consistent methods for order and coefficient estimation of continuous-time errors-in-variables fractional models Article de journal Dans: Computers and Mathematics with Applications, vol. 66, no. 5, p. 860 – 872, 2013, (Cited by: 30; All Open Access, Bronze Open Access). Résumé | Liens | BibTeX | Étiquettes: Algorithms, commensurate order, Differential equations, Errors, Errors in variables, Estimation, Fractional differentiation, Higher order statistics, Identification (control systems), Identification problem, Iterative least squares, Least squares algorithm, Non-linear optimization algorithms, Third-order cumulant @article{Chetoui2013860,The errors-in-variables identification problem concerns dynamic systems in which input and output signals are contaminated by an additive noise. Several estimation methods have been proposed for identifying dynamic errors-in-variables rational models. This paper presents new consistent methods for order and coefficient estimation of continuous-time systems by errors-in-variables fractional models. First, differentiation orders are assumed to be known and only differential equation coefficients are estimated. Two estimators based on Higher-Order Statistics (third-order cumulants) are developed: the fractional third-order based least squares algorithm (ftocls) and the fractional third-order based iterative least squares algorithm (ftocils). Then, they are extended, using a nonlinear optimization algorithm, to estimate both the differential equation coefficients and the commensurate order. The performances of the proposed algorithms are illustrated with a numerical example. |
2012 |
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Aoun, Mohamed; Najar, Slaheddine; Oustaloup, Alain; Abdelkrim, Mohamed Naceur EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Chetoui20121641b,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. |
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Aoun, Mohamed; Najar, Slaheddine; Oustaloup, Alain; Abdelkrim, Mohamed Naceur EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Chetoui20121641,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. |
Publications
2019 |
2019, (Cited by: 5). |
2019, (Cited by: 5). |
2014 |
2014, (Cited by: 0). |
2014, (Cited by: 0). |
2013 |
Fourth-order cumulants based method for continuous-time EIV fractional model identification Conférence 2013, (Cited by: 4). |
New consistent methods for order and coefficient estimation of continuous-time errors-in-variables fractional models Article de journal Dans: Computers and Mathematics with Applications, vol. 66, no. 5, p. 860 – 872, 2013, (Cited by: 30; All Open Access, Bronze Open Access). |
Fourth-order cumulants based method for continuous-time EIV fractional model identification Conférence 2013, (Cited by: 4). |
New consistent methods for order and coefficient estimation of continuous-time errors-in-variables fractional models Article de journal Dans: Computers and Mathematics with Applications, vol. 66, no. 5, p. 860 – 872, 2013, (Cited by: 30; All Open Access, Bronze Open Access). |
2012 |
EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). |
EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). |