Browsing by Author "Kutlu, F."

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Beam and Ball Plant System Controlling Using Intuitionistic Fuzzy Control
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtar, O.; Atan, Ö.; Kutlu, F.; Castillo, O.
In this study, simplified “beam and ball (BNB) system” is controlled using “intuitionistic fuzzy control (IFC)” method. It is aimed to keep the ball on it in balance by applying DC voltage in different magnitudes to the DC motor of the system called “ball and beam plant”, which has a beam on which a DC motor is attached to the middle point and a ball moving without friction. In order to better observe the effect of this new generation controller applied to the system, parameters such as the torque of the motor, the mass of the beam and the ball, internal and external disturbance, friction etc. were ignored and the system is simplified. The position and velocity of the ball on the beam is taken as input for the controller, while the controller output is chosen as a DC voltage. After I-Fuzzification, I-Inference and I-Defuzzification process are performed in controller block, performance and efficiency of the system are discussed in terms of steady state error, setting time, maximum overshoot, chattering. © 2021, IFIP International Federation for Information Processing.
Integrating Fuzzy Metrics and Negation Operator in Fcm Algorithm Via Genetic Algorithm for Mri Image Segmentation
(Springer Science and Business Media Deutschland GmbH, 2024) Kutlu, F.; Ayaz, İ.; Garg, H.
In this study, we redefine FCM algorithm by integrating fuzzy set theory, fuzzy metrics, and Sugeno negation principles. This innovative approach overcomes the limitations inherent in conventional machine learning models, especially in situations characterized by uncertainty, noise, and ambiguity. Our model utilizes the membership degrees from fuzzy set theory, and transforms the concept of proximity defined by fuzzy metrics into a minimization problem. This transformation is achieved using a linguistic negation operator, which is crucial for optimizing FCM algorithm's objective function. A significant innovation in our research is the use of GA for optimizing parameters within the contexts of fuzzy metrics and Sugeno negation. The precise optimization capabilities of GA greatly enhance the sensitivity and adaptability of FCM algorithm, thereby improving overall performance. By leveraging the meticulous parameter adjustments provided by GA, our approach has shown superior results in practical applications, such as brain MRI image segmentation, surpassing traditional methods. Experimental results highlight the considerable enhancements our proposed FCM algorithms bring over existing methods across various performance metrics. In conclusion, this study makes a valuable addition to the field of fuzzy-based machine learning methodologies. It combines the optimization strength of GA with the flexible classification capabilities of fuzzy logic. The integration of Sugeno negation and fuzzy metrics not only improves the accuracy and precision of FCM algorithm but also provides significant benefits in handling complex and ambiguous datasets. This research signifies a major advance in machine learning and fuzzy logic, setting the stage for future applications and studies. © The Author(s) 2024.
Rendezvous and Docking Control of Satellites Using Chaos Synchronization Method With Intuitionistic Fuzzy Sliding Mode Control
(Springer Science and Business Media Deutschland GmbH, 2023) Silahtar, O.; Kutlu, F.; Atan, Ö.; Castillo, O.
In this study, two different controllers have been designed to perform the rendezvous and docking tasks of two nonidentical and noncooperative cubic satellites. Firstly, the motion of cubic satellites was modeled with chaotic equations. After selecting suitable chaotic models, fuzzy sliding mode controller (FSMC) and a new intuitionistic fuzzy sliding mode controller (IFSMC), which are applied to synchronization systems under the same initial conditions, have been designed. It has been observed that both synchronizations reach stability by applying the controllers designed by considering the Lyapunov stability criteria. After a while, a short-term and random disturbance was applied to the synchronization systems and the response of both controllers was observed. The numerical results showed that the synchronization system with both controllers was stable, robust, efficient, fast and chattering-free. However, synchronization system with IFSMC was found to be more robust, faster and more efficient than synchronization system with FSMC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.