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

Victor, Stéphane; Duhé, Jean-François; Melchior, Pierre; Abdelmounen, Youssef; Roubertie, François

doi:10.1007/s11071-022-07628-8

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article5

Preprint1

Relationship

Self Cite0

Independent6

Authors

Journals

Cited by 14 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some progress has been made in estimating the order of fractional order systems. However, there is still difficult to estimate both model coefficients and noninteger orders [10].…”

Section: Introductionmentioning

confidence: 99%

Parametric identification of fractional order system using Bayesian method

Jin

Zhang

Zhao

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, the estimation issue for fractional order system with unknown parameter and order is addressed. The Bayesian method is used for system identification. First, the framework of parameter identification algorithm is built and the conjugate prior is constructed. Then, an improved descent method is used to update the posterior distribution based on the idea of maximum likelihood function. The proposed method solves the problem of overall identification of order and parameters, and can also estimate the noise in the system. Finally, numerical experiments comparing with the optimization algorithm are completed. Experiments on prediction performance, convergence ability and time complexity verify the effectiveness and speed of the proposed algorithm.

show abstract

“…Some progress has been made in estimating the order of fractional order systems. However, there is still difficult to estimate both model coefficients and noninteger orders [10].…”

Section: Introductionmentioning

confidence: 99%