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Contribution to the initialization of linear non-commensurate fractional-order systems for the joint estimation of parameters and fractional differentiation orders

Contribution to the initialization of linear non-commensurate fractional-order systems for the joint estimation of parameters and fractional differentiation orders
Contribution to the initialization of linear non-commensurate fractional-order systems for the joint estimation of parameters and fractional differentiation orders
It has been recognized that using time-varying initialization functions to solve the initial value problem of fractional-order systems (FOS) is both complex and essential in defining the dynamical behavior of the states of FOSs. In this paper, we investigate the use of the initialization functions for the purpose of estimating unknown parameters of linear non-commensurate FOSs. In particular, we propose a novel "pre-initial" process that describes the dynamic characteristic of FOSs before the initial state and consists of designing an appropriate time-varying initialization function that ensures accurate convergence of the estimates of the unknown parameters. To do so, we propose an estimation technique that consists of two steps: (i) to design of practical initialization function that is output-dependent and which is employed; (ii) to solve the joint estimation problem of both parameters and fractional differentiation orders (FDOs). A convergence proof has been presented. The performance of the proposed method is illustrated through different numerical examples. Potential applications of the algorithm to joint estimation of parameters and FDOs of the fractional arterial Windkessel and neurovascular models are also presented using both synthetic and real data. The added value of the proposed "pre-initial" process to solve the studied estimation problem is shown through different simulation tests that investigate the sensitivity of estimation results using different time-varying initialization functions.
Statistics - Methodology, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
arXiv
Bahloul, Mohamed A.
fa34d4d5-5861-4104-9cf8-f4dec80c84a3
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
laleg-Kirati, Taous-Meriem
0363864e-a21e-44ed-9c9d-f43f3491b758
Bahloul, Mohamed A.
fa34d4d5-5861-4104-9cf8-f4dec80c84a3
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
laleg-Kirati, Taous-Meriem
0363864e-a21e-44ed-9c9d-f43f3491b758

[Unknown type: UNSPECIFIED]

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Abstract

It has been recognized that using time-varying initialization functions to solve the initial value problem of fractional-order systems (FOS) is both complex and essential in defining the dynamical behavior of the states of FOSs. In this paper, we investigate the use of the initialization functions for the purpose of estimating unknown parameters of linear non-commensurate FOSs. In particular, we propose a novel "pre-initial" process that describes the dynamic characteristic of FOSs before the initial state and consists of designing an appropriate time-varying initialization function that ensures accurate convergence of the estimates of the unknown parameters. To do so, we propose an estimation technique that consists of two steps: (i) to design of practical initialization function that is output-dependent and which is employed; (ii) to solve the joint estimation problem of both parameters and fractional differentiation orders (FDOs). A convergence proof has been presented. The performance of the proposed method is illustrated through different numerical examples. Potential applications of the algorithm to joint estimation of parameters and FDOs of the fractional arterial Windkessel and neurovascular models are also presented using both synthetic and real data. The added value of the proposed "pre-initial" process to solve the studied estimation problem is shown through different simulation tests that investigate the sensitivity of estimation results using different time-varying initialization functions.

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Published date: 18 October 2022
Keywords: Statistics - Methodology, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control

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Local EPrints ID: 509791
URI: http://eprints.soton.ac.uk/id/eprint/509791
PURE UUID: 8b960307-0bfe-44bf-a307-149900e5628b
ORCID for Zehor Belkhatir: ORCID iD orcid.org/0000-0001-7277-3895

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Date deposited: 05 Mar 2026 22:59
Last modified: 06 Mar 2026 03:25

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Contributors

Author: Mohamed A. Bahloul
Author: Zehor Belkhatir ORCID iD
Author: Taous-Meriem laleg-Kirati

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