On Ensemble Learning Models for Autoencoder-Based Identification of Nonlinear Dynamical Systems

Identifying the dynamic model of a system has been a subject of extensive research for decades. However, dealing with highly complex, possibly nonlinear, and noisy systems remains an open problem. In recent years, the use of data-driven techniques based on machine learning for system identification...

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Bibliographic Details
Published in:	IEEE access Vol. 13; pp. 169071 - 169082
Main Authors:	Arridu, Nicola, Lippi, Martina, Franceschelli, Mauro, Gasparri, Andrea
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Autoencoders Biological system modeling Complex systems Data models data-driven analysis Decoding Dynamic models Dynamical systems Ensemble learning Machine learning Neural networks Nonlinear dynamical systems Nonlinear system identification Nonlinear systems Numerical models System dynamics System identification
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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