Long-memory recursive prediction error method for identification of continuous-time fractional models
Language
EN
Article de revue
This item was published in
Nonlinear Dynamics. 2022-06-25, vol. 110, n° 1, p. 635-648
English 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 ...Read more >
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.Read less <
Keywords
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