Publications

@conference{Aoun2012b,

title = {Continuous fractional Kalman filter},

author = {Mohamed Aoun and Slaheddine Najar and Moufida Abdelhamid and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861605131\&doi=10.1109%2fSSD.2012.6198068\&partnerID=40\&md5=3b5a3d80fe7b60e0ca04ee80eddf2959},

doi = {10.1109/SSD.2012.6198068},

year  = {2012},

date = {2012-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD 2012 - Summary Proceedings},

abstract = {This paper develops a new Kalman filter for linear systems described with continuous time fractional model. It extends the classical Kalman filter to deals with fractional differentiation. It is called continuous fractional Kalman Filter. The algorithm of the new filter is detailed and a suboptimal filter can be deduced. A numerical example illustrates the state estimation of a fractional model with the new filter. © 2012 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time, Fractional differentiation, Fractional model, Kalman filters, Linear systems, Numerical example, State estimation, Suboptimal filter},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aoun2012,

title = {Continuous fractional Kalman filter},

author = {Mohamed Aoun and Slaheddine Najar and Moufida Abdelhamid and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861605131\&doi=10.1109%2fSSD.2012.6198068\&partnerID=40\&md5=3b5a3d80fe7b60e0ca04ee80eddf2959},

doi = {10.1109/SSD.2012.6198068},

year  = {2012},

date = {2012-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD 2012 - Summary Proceedings},

abstract = {This paper develops a new Kalman filter for linear systems described with continuous time fractional model. It extends the classical Kalman filter to deals with fractional differentiation. It is called continuous fractional Kalman Filter. The algorithm of the new filter is detailed and a suboptimal filter can be deduced. A numerical example illustrates the state estimation of a fractional model with the new filter. © 2012 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time, Fractional differentiation, Fractional model, Kalman filters, Linear systems, Numerical example, State estimation, Suboptimal filter},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aoun2011f,

title = {On the fractional systems' fault detection: A comparison between fractional and rational residual sensitivity},

author = {Mohamed Aoun and Asma Aribi and Slaheddine Najar and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957903547\&doi=10.1109%2fSSD.2011.5767424\&partnerID=40\&md5=2e22d74f672e2ae986db94c4dbcd72e6},

doi = {10.1109/SSD.2011.5767424},

year  = {2011},

date = {2011-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD'11 - Summary Proceedings},

abstract = {This paper shows the interest of extending the dynamic parity space fault detection method for fractional systems. Accordingly, a comparison between fractional and rational residual generators using the later method is presented. An analysis of fractional and rational residuals' sensitivity shows the merits of the fractional residual generators. A numerical example illustrating the advantage of using fractional residual generators for fractional systems diagnosis is given. © 2011 IEEE.},

note = {Cited by: 8},

keywords = {Detection methods, Fault detection, fault diagnosis, Fractional derivatives, Fractional systems, Numerical example, Parity spaces, Residual generator, residual sensitivity, Signal detection},

pubstate = {published},

tppubtype = {conference}

}

@conference{Abdelhamid2009b,

title = {Discrete fractional Kalman filter},

author = {Moufida Abdelhamid and Mohamed Aoun and Slaheddine Najar and Mohamed Naceur Abdelhamid},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955725313\&doi=10.3182%2f20090921-3-TR-3005.00090\&partnerID=40\&md5=13ac1eb2213053d7b1a48169500ba641},

doi = {10.3182/20090921-3-TR-3005.00090},

year  = {2009},

date = {2009-01-01},

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

volume = {2},

number = {PART 1},

abstract = {This paper presents a generalization of the classical discrete time Kalman filter algorithm to the case of the fractional systems. Motivations for the use of this filter are given and the algorithm is detailed. The document also shows a simple numerical example of linear state estimation. Copyright © 2007 International Federation of Automatic Control.},

note = {Cited by: 20; All Open Access, Bronze Open Access},

keywords = {Algorithms, Discrete time control systems, Discrete-time Kalman filters, Fractional differentiation, Fractional Kalman filter, Fractional kalman filters, Fractional systems, Intelligent control, Kalman filters, Linear state estimation, Numerical example, Signal processing, State estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Abdelhamid2009,

title = {Discrete fractional Kalman filter},

author = {Moufida Abdelhamid and Mohamed Aoun and Slaheddine Najar and Mohamed Naceur Abdelhamid},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955725313\&doi=10.3182%2f20090921-3-TR-3005.00090\&partnerID=40\&md5=13ac1eb2213053d7b1a48169500ba641},

doi = {10.3182/20090921-3-TR-3005.00090},

year  = {2009},

date = {2009-01-01},

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

volume = {2},

number = {PART 1},

abstract = {This paper presents a generalization of the classical discrete time Kalman filter algorithm to the case of the fractional systems. Motivations for the use of this filter are given and the algorithm is detailed. The document also shows a simple numerical example of linear state estimation. Copyright © 2007 International Federation of Automatic Control.},

note = {Cited by: 20; All Open Access, Bronze Open Access},

keywords = {Algorithms, Discrete time control systems, Discrete-time Kalman filters, Fractional differentiation, Fractional Kalman filter, Fractional kalman filters, Fractional systems, Intelligent control, Kalman filters, Linear state estimation, Numerical example, Signal processing, State estimation},

pubstate = {published},

tppubtype = {conference}

}