Yazar "Shahbazova, S.N." seçeneğine göre listele

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    Balance-Based Fuzzy Logic Approach for the Classification of Liver Diseases Due to Hepatitis C Virus
    (Springer Science and Business Media Deutschland GmbH, 2023) Ekmekci, D.; Shahbazova, S.N.
    Fuzzy logic can be successfully applied in the many fields of artificial intelligence, such as control, classification, clustering, and prediction. However, assigning the optimal values for the control parameters and designing the optimal fuzzy rule table is vital to the method’s performance. Besides, increasing the number of variables increases the solution space combinatorically. This study proposes a novel method that uses the fuzzy logic approach. The method positions the inputs and outputs on the unit circle and aims to balance them at the circle origin. The efficiency and performance of the method were investigated in the problem of classification of Liver Diseases Due to Hepatitis C Virus. The results prove that the method can design successful systems for classification problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    An Input-weighted, Multi-Objective Evolutionary Fuzzy Classifier, for Alcohol Classification
    (Budapest Tech Polytechnical Institution, 2022) Shahbazova, S.N.; Ekmekci, D.
    The success of the evolutionary computational methods in scanning at problem's solution space and the ability to produce robust solutions, are important advantages for fuzzy systems, especially in terms of "interpretability" and "accuracy". Many techniques have been introduced for multi-objective evolutionary fuzzy classifiers by considering this advantage. However, these techniques are mostly fuzzy rule-based methods. In this study, instead of designing an optimal rule table or determining optimal rule weights, the inputs are weighted, and no rules are used. The average of the degrees of membership obtained with their Membership Function (MF) is calculated as the "input membership degree (?Inp)" for each input. The ?Inps are then weighted, and a single coefficient is generated to be used for the output. With the output, results are obtained for different objective functions. The weights of the inputs and the MFs parameters of all variables (inputs and outputs) are optimized with NSGA-II. The performance of the method has been tested for alcohol classification. As a result, it has been proven that the method can generate designs that can classify at shallow error levels with different sensors at different gas concentrations. In addition, it has been observed that the proposed method produces more successful solutions for alcohol classification problems when compared to other MOEFC techniques. © 2022, Budapest Tech Polytechnical Institution. All rights reserved.
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    Tuning of Fuzzy Control Systems by Artificial Bee Colony with Dynamic Parameter Values Algorithm for Traction Power System
    (Springer Science and Business Media Deutschland GmbH, 2023) Shahbazova, S.N.; Ekmekci, D.
    Traction power supply systems are one of the main parameters for high-speed train transportation, which has become increasingly common recently. The system can be controlled by a combination of different techniques. Simulation modeling shows that traction power supply systems make it possible to improve the power quality and reduce power losses in traction substations. This study proposes a fuzzy control system (FCS) using the Sugeno inference system to control the traction power supply system. Artificial Bee Colony with Dynamic Parameter Values (ABC-DPV) algorithm was used in the parameter optimization of MFs and rule selection of the designed system. ABC-DPV is a version of ABC that dynamically updates the parameter values of the algorithm in the search process to improve the exploitation and exploration capability of the algorithm. The proposed FCS has been tested on samples obtained from the simulation results, and it produces reasonable solutions at negligible error levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.