VICTOR, Stéphane; DUHE, Jean Francois; MELCHIOR, Pierre; ABDELMOUNEN, Youssef; ROUBERTIE, François

doi:10.1007/s11071-022-07628-8

Laboratoire de l'intégration, du matériau au système [IMS]

DUHE, Jean Francois

Laboratoire de l'intégration, du matériau au système [IMS]

MELCHIOR, Pierre
Laboratoire de l'intégration, du matériau au système [IMS]

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Nonlinear Dynamics. 2022-06-25, vol. 110, n° 1, p. 635-648

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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.< Leer menos

Palabras clave

Continuous-time models

Fractional calculus

Fractional-order model

System identification

Recursive identification

Real-time system identification

Prediction error method

Least squares

Long-memory prediction error method

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https://oskar-bordeaux.fr/handle/20.500.12278/170342

DOI

http://dx.doi.org/10.1007/s11071-022-07628-8

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

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

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DOI

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