5 citations · 7 across the 19 of their papers we have counts for
20 papers
Physics-Informed Bayesian Optimization Warm-Starts for Sequential Convex Programming in Asteroid Surface Hopping
Baran Ekşi, Tufan Kumbasar
Surface hopping is an attractive mobility mode for small-body exploration, but designing fuel-optimal hops on asteroid 433~Eros requires solving a nonconvex optimal control problem…
Handover Analysis for Vehicular Communication with Explainability on the Fly
Ali Fuat Sahin, Semiha Tedik Başaran, Tufan Kumbasar
Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO det…
Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
Mehmet Ali Ferah, Tufan Kumbasar
System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlinear behaviors. While deep le…
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…
SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
Murat Furkan Mansur, Tufan Kumbasar
Nonlinear system identification must balance physical interpretability with model flexibility. Classical methods yield structured, control-relevant models but rely on rigid paramet…
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…
Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy
Yusuf Guven, Tufan Kumbasar
Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications. Recently, General Type-2 Fuzzy Logic Systems (GT2-FLSs) hav…
FAME: Introducing Fuzzy Additive Models for Explainable AI
Omer Bahadir Gokmen, Yusuf Guven, Tufan Kumbasar
In this study, we introduce the Fuzzy Additive Model (FAM) and FAM with Explainability (FAME) as a solution for Explainable Artificial Intelligence (XAI). The family consists of th…
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework
Ertuğrul Keçeci, Müjde Güzelkaya, Tufan Kumbasar
This paper presents FedAlign, a Federated Learning (FL) framework particularly designed for System Identification (SYSID) tasks by aligning state representations. Local workers can…
Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density Estimation
Batuhan Cengiz, Halil Faruk Karagoz, Tufan Kumbasar
Recent methods in quantile regression have adopted a classification perspective to handle challenges posed by heteroscedastic, multimodal, or skewed data by quantizing outputs into…
Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals
Ata Koklu, Yusuf Guven, Tufan Kumbasar
In this paper, we tackle the task of generating Prediction Intervals (PIs) in high-risk scenarios by proposing enhancements for learning Interval Type-2 (IT2) Fuzzy Logic Systems (…
Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals
Yusuf Guven, Ata Koklu, Tufan Kumbasar
General Type-2 (GT2) Fuzzy Logic Systems (FLSs) are perfect candidates to quantify uncertainty, which is crucial for informed decisions in high-risk tasks, as they are powerful too…
Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning
Ata Koklu, Yusuf Guven, Tufan Kumbasar
Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure. Yet, in learning large-scale data challe…
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward
M. Ugur Yavas, N. Kemal Ure, Tufan Kumbasar
Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature. This paper present…