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xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification
Ertugrul Kececi, Tufan Kumbasar
Recent advances in Deep Learning (DL) have strengthened data-driven System Identification (SysID), with Neural and Fuzzy Ordinary Differential Equation (NODE/FODE) models achieving…
xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
Ertugrul Kececi, Tufan Kumbasar
Recent advances in Deep Learning (DL) have boosted data-driven System Identification (SysID), but reliable use requires Uncertainty Quantification (UQ) alongside accurate predictio…
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework
ErtuÄrul Keçeci, Ertuğrul Keçeci, Müjde Güzelkaya +2
This paper presents FedAlign, a Federated Learning (FL) framework particularly designed for System Identification (SYSID) tasks by aligning state representations. Local workers can…
Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft
ErtuÄrul Keçeci, Müjde Güzelkaya, Tufan Kumbasar
This paper addresses the System Identification (SYSID) problem within the framework of federated learning. We introduce a novel algorithm, Incremental Clustering-based federated le…