1 citations · 2 across the 8 of their papers we have counts for
10 papers
Stable-by-Design Neural Network-Based LPV State-Space Models for System Identification
Ahmet Eren Sertbaş, Tufan Kumbasar
Accurate modeling of nonlinear systems is essential for reliable control, yet conventional identification methods often struggle to capture latent dynamics while maintaining stabil…
Introducing Fractional Classification Loss for Robust Learning with Noisy Labels
Mert Can Kurucu, Tufan Kumbasar, İbrahim Eksin +1
Robust loss functions are crucial for training deep neural networks in the presence of label noise, yet existing approaches require extensive, dataset-specific hyperparameter tunin…
Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft
Ertuğrul 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…
Capturing Aerodynamic Characteristics of ATTAS Aircraft with Evolving Intelligent System
Aydoğan Soylu, Tufan Kumbasar
Accurate modeling of aerodynamic coefficients is crucial for understanding and optimizing the performance of modern aircraft systems. This paper presents the novel deployment of an…
Imitation Learning for Autonomous Driving: Insights from Real-World Testing
Hidayet Ersin Dursun, Yusuf Güven, Tufan Kumbasar
This work focuses on the design of a deep learning-based autonomous driving system deployed and tested on the real-world MIT Racecar to assess its effectiveness in driving scenario…
Introducing Interval Neural Networks for Uncertainty-Aware System Identification
Mehmet Ali Ferah, Tufan Kumbasar
System Identification (SysID) is crucial for modeling and understanding dynamical systems using experimental data. While traditional SysID methods emphasize linear models, their in…