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}

}