Yazar "Menguc E.C." seçeneğine göre listele

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    Automatic removal of ocular artefacts in EEG signal by using independent component analysis and artificial neural network
    (Institute of Electrical and Electronics Engineers Inc., 2017) Cinar S.; Menguc E.C.; Acir N.
    Ocular artefacts caused by eye movements can distort Electroencephalogram (EEG) recordings. It is important to obtain clean EEG signals in diagnosing and interpreting diseases. Meaningful EEG signals should not be distorted during the removal of artefacts. In this study, Independent Component Analysis and Artificial Neural Network were used together to remove ocular artefacts. The method was tested by using the real dataset. The Relative Error (RE) and Correlation Coefficient (CC) was used to test the performance of the method. Relative error = 0.227±0.229 and correlation coefficient = 0.941 ±0.088 was calculated in the performance analysis. According to the results, the proposed method is successful in removing ocular artefacts in EEG signals. © 2017 IEEE.
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    Comparison of split complex-valued metaheuristic optimization algorithms for system identification problem [Sistem tanimlama problemi için bölünmüş kompleks-degerli sezgisel eniyileme algoritmalarinin karşilaştirilmasi]
    (Institute of Electrical and Electronics Engineers Inc., 2018) Menguc E.C.; Peker M.; Cinar S.
    Since some of the real world problems include phase and amplitude information, complex modeling is more suitable. In this study, the well-used particle swarm optimization, simulated annealing and genetic algorithm are designed in a split form in order to process complex-valued signals. The performances of the algorithms are comparatively tested on two different system identification problems for different noise levels. Simulation results show that the split complex-valued metaheuristic algorithms produce results which are almost close to the weights of both unknown systems. © 2018 IEEE.
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    Kurtosis-Based CRTRL Algorithms for Fully Connected Recurrent Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2018) Menguc E.C.; Acir N.
    In this paper, kurtosis-based complex-valued real-time recurrent learning (KCRTRL) and kurtosis-based augmented CRTRL (KACRTRL) algorithms are proposed for training fully connected recurrent neural networks (FCRNNs) in the complex domain. These algorithms are designed by minimizing the cost functions based on the kurtosis of a complex-valued error signal. The KCRTRL algorithm exploits the circularity properties of the complex-valued signals, and this algorithm not only provides a faster convergence rate but also results in a lower steady-state error. However, the KCRTRL algorithm is suboptimal in the processing of noncircular (NC) complex-valued signals. On the other hand, the KACRTRL algorithm contains a complete second-order information due to the augmented statistics, thus considerably improves the performance of the FCRNN in the processing of NC complex-valued signals. Simulation results on the one-step-ahead prediction problems show that the proposed KCRTRL algorithm significantly enhances the performance for only circular complex-valued signals, whereas the proposed KACRTRL algorithm provides more superior performance than existing algorithms for NC complex-valued signals in terms of the convergence rate and the steady-state error. © 2012 IEEE.