Long-memory recursive prediction error method for identification of continuous-time fractional models - Archive ouverte HAL Access content directly
Journal Articles Nonlinear Dynamics Year : 2022

Long-memory recursive prediction error method for identification of continuous-time fractional models

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Abstract

This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models.
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Dates and versions

hal-03865862 , version 1 (22-11-2022)

Licence

Attribution - CC BY 4.0

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Cite

Stephane Victor, Jean Francois Duhe, Pierre Melchior, Youssef Abdelmounen, François Roubertie. Long-memory recursive prediction error method for identification of continuous-time fractional models. Nonlinear Dynamics, 2022, 110 (1), pp.635-648. ⟨10.1007/s11071-022-07628-8⟩. ⟨hal-03865862⟩
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