A set-Membership Identification Algorithm for Time-Series Modeling Using ARMA Process
This paper focuses on the identification of ARMA models for time-series analysis. The proposed approach is developed in the Unknown But Bounded Error framework. Two Ellipsoidal Outer Bounding (EOB) identification algorithms are proposed. The second algorithm is an extended version of the first one. The paper includes stability and convergence analysis. Some numerical simulations are conducted to validate the analysis and assess the effectiveness of the proposed algorithms.
Authors
- Miloud Frikel (ORCID: https://orcid.org/0000-0003-2178-1814)
- Saïd Safi (ORCID: https://orcid.org/0000-0003-3390-9037)
- Mathieu Pouliquen (ORCID: https://orcid.org/0000-0002-2186-986X)
- Hasna El Maizi
Institutions
- École Nationale Supérieure d'Ingénieurs de Caen (FR)
- Normandie Université (FR)
- Laboratoire d’Informatique et Systèmes (FR)
- Université Sultan Moulay Slimane (MA)
- Université de Caen Normandie (FR)
Publication Details
- Published
- 2024-11-03
- DOI
- https://doi.org/10.1109/icsc63929.2024.10928846
- Citations
- 1
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.35