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Aykut Beke
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- affiliation: Istanbul Technical University, Turkey
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2020 – today
- 2024
- [j8]Fethi Candan
, Aykut Beke, Mahdi Mahfouf, Lyudmila Mihaylova:
A Real-time Fuzzy Interacting Multiple-Model Velocity Obstacle Avoidance Approach for Unmanned Aerial Vehicles. J. Intell. Robotic Syst. 110(2): 61 (2024) - 2023
- [j7]Aykut Beke
More Than Accuracy: A Composite Learning Framework for Interval Type-2 Fuzzy Logic Systems. IEEE Trans. Fuzzy Syst. 31(3): 734-744 (2023) - [c11]Batuhan Avci, Aykut Beke, Tufan Kumbasar
Towards Reliable Uncertainty Quantification and High Precision with General Type-2 Fuzzy Systems. FUZZ 2023: 1-6 - [c10]Fethi Candan
An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter. IROS 2023: 3314-3321 - 2022
- [j6]Turcan Tuna, Aykut Beke, Tufan Kumbasar
Deep learning frameworks to learn prediction and simulation focused control system models. Appl. Intell. 52(1): 662-679 (2022) - [j5]Turcan Tuna, Aykut Beke, Tufan Kumbasar
Correction to: Deep learning frameworks to learn prediction and simulation focused control system models. Appl. Intell. 52(1): 680 (2022) - [c9]Fethi Candan
An Interacting Multiple Model Correntropy Kalman Filter Approach for Unmanned Aerial Vehicle Localisation. INISTA 2022: 1-6 - 2021
- [j4]Ugur Bugra Etlik, Berk Korkmaz, Aykut Beke
A fuzzy logic-based autonomous car control system for the JavaScript Racer game. Trans. Inst. Meas. Control 43(5): 1028-1038 (2021) - [c8]Aykut Beke, Tufan Kumbasar
Capturing Uncertainty with Interval Fuzzy Logic Systems through Composite Deep Learning. FUZZ-IEEE 2021: 1-6 - 2020
- [j3]Dorukhan Erdem
A Deep Learning-Based Pipeline for Teaching Control Theory: Transforming Feedback Control Systems on Whiteboard Into MATLAB. IEEE Access 8: 84631-84641 (2020) - [j2]Aykut Beke
Type-2 Fuzzy Logic-Based Linguistic Pursuing Strategy Design and Its Deployment to a Real-World Pursuit Evasion Game. IEEE Trans. Cybern. 50(1): 211-221 (2020)
2010 – 2019
- 2019
- [j1]Aykut Beke
Learning with Type-2 Fuzzy activation functions to improve the performance of Deep Neural Networks. Eng. Appl. Artif. Intell. 85: 372-384 (2019) - [c7]Aykut Beke, Tufan Kumbasar
Interval Type-2 Fuzzy Systems as Deep Neural Network Activation Functions. EUSFLAT Conf. 2019 - 2018
- [c6]Aykut Beke
Single Vs. Double Input Interval Type-2 Fuzzy PID Controllers: Which One is Better? FUZZ-IEEE 2018: 1-7 - [c5]Ahmet Sakalli, Aykut Beke
Analyzing the Control Surfaces of Type-1 and Interval Type-2 FLCs through an Experimental Study. FUZZ-IEEE 2018: 1-6 - [c4]Fethi Candan
Design and Deployment of Fuzzy PID Controllers to the nano quadcopter Crazyflie 2.0. INISTA 2018: 1-6 - 2017
- [c3]Aykut Beke
Game of spheros: A real-world pursuit-evasion game with type-2 fuzzy logic. FUZZ-IEEE 2017: 1-6 - 2016
- [c2]Ahmet Sakalli, Aykut Beke
Gradient Descent and Extended Kalman Filter based self-tuning Interval Type-2 Fuzzy PID controllers. FUZZ-IEEE 2016: 1592-1598 - 2015
- [c1]Akin Ilker Savran, Aykut Beke
An IMC based fuzzy self-tuning mechanism for fuzzy PID controllers. INISTA 2015: 1-7
Coauthor Index
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