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{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{Yakoub2017271b,

title = {A comparison between the direct and the indirect fractional order closed-loop bias eliminated least squares method},

author = {Z. Yakoub and M. Chetoui and M. Amairi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024378403\&doi=10.1109%2fSTA.2016.7951978\&partnerID=40\&md5=f96317e305a1fb9879331639139ecc89},

doi = {10.1109/STA.2016.7951978},

year  = {2017},

date = {2017-01-01},

journal = {2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings},

pages = {271 \textendash 282},

abstract = {This paper deals with the fractional closed-loop system identification. A comparison between the direct and the indirect approach is processed. The fractional order bias eliminated least squares method is used to identify the fractional closed-loop transfer function. This method is founded on the ordinary least squares method and the state variable filter. A numerical example is treated to show the efficiency of each approach and discuss results. © 2016 IEEE.},

note = {Cited by: 0},

keywords = {Automation, Closed loops, Direct approach, Fractional differentiation, Identification (control systems), indirect approach, Least Square, Least squares approximations, Process control},

pubstate = {published},

tppubtype = {conference}

}

@conference{Yakoub2015e,

title = {A direct fractional order bias eliminated least squares method for the fractional closed-loop system identification},

author = {Z. Yakoub and M. Chetoui and M. Amairi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962688308\&doi=10.1109%2fSSD.2015.7348219\&partnerID=40\&md5=1523b25860bd1afff62b4ced5a164cdd},

doi = {10.1109/SSD.2015.7348219},

year  = {2015},

date = {2015-01-01},

journal = {12th International Multi-Conference on Systems, Signals and Devices, SSD 2015},

abstract = {The paper deals with the continuous-time fractional closed-loop system identification in a noisy output context. Both coefficients and fractional orders of the process are estimated using the direct approach. The proposed method is based on the least squares technique and the state variable filter. It is an extension of the bias eliminated least squares method to the fractional systems. It is combined to a nonlinear optimization algorithm in order to estimate both coefficients and fractional orders of the fractional process. A numerical example is presented to illustrate the performances of the proposed methods. © 2015 IEEE.},

note = {Cited by: 2},

keywords = {Algorithms, Closed loop systems, Closed loops, Continuous time systems, Continuous-time, Direct approach, Fractional differentiation, Identification (control systems), Least Square, Least squares approximations, Nonlinear programming, Numerical methods, Optimization, Religious buildings, State-variable filters},

pubstate = {published},

tppubtype = {conference}

}

@conference{Yakoub2014c,

title = {Indirect approach for closed-loop system identification with fractional models},

author = {Z. Yakoub and M. Chetoui and M. Amairi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918521162\&doi=10.1109%2fICFDA.2014.6967441\&partnerID=40\&md5=d666e64f7fc6c6a967f8001f8f810bf2},

doi = {10.1109/ICFDA.2014.6967441},

year  = {2014},

date = {2014-01-01},

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

abstract = {This paper deals with fractional closed-loop system identification using the indirect approach. Firstly, all differentiation orders are supposed known and only the coefficients of the closed-loop fractional transfer function are estimated using two methods based on least squares techniques. Then, the fractional open-loop process is determined by the knowledge of the regulator. A numerical example is presented to show the effectiveness of the proposed scheme. © 2014 IEEE.},

note = {Cited by: 4},

keywords = {Calculations, Closed loop systems, Closed loops, Differentiation (calculus), Fractional calculus, Fractional model, Identification (control systems), Least Square, Least squares approximations, Least squares techniques, Open-loop process, Religious buildings, State-variable filters},

pubstate = {published},

tppubtype = {conference}

}

@conference{Yakoub2014128b,

title = {A bias-eliminated least squares method for continuous-time fractional closed-loop system identification},

author = {Z. Yakoub and M. Amairi and M. Chetoui and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920520477\&doi=10.1109%2fCCA.2014.6981340\&partnerID=40\&md5=1e18e1e8dcd7b25d35f5589188cfe8a6},

doi = {10.1109/CCA.2014.6981340},

year  = {2014},

date = {2014-01-01},

journal = {2014 IEEE Conference on Control Applications, CCA. Part of 2014 IEEE Multi-conference on Systems and Control, MSC 2014},

pages = {128 \textendash 133},

abstract = {This paper deals with continuous-time fractional closed-loop system identification in a noisy output context. A bias correction method called the bias-eliminated least squares is extended for indirect approach identification of closed-loop system with fractional models. This method is based on the least squares method combined with the state variable filter and assumes that the regulator order can not be lower than the process order. The performances of the proposed method are assessed through a numerical example. © 2014 IEEE.},

note = {Cited by: 5},

keywords = {Closed loop systems, Closed loops, Continuous time systems, Continuous-time, Fractional differentiation, Identification (control systems), Least Square, Least squares approximations, Numerical methods, Religious buildings, State-variable filters},

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 = {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}

}