Yazar "Menguc, Engin Cemal" seçeneğine göre listele

Listeleniyor 1 - 11 / 11
  • Küçük Resim Yok
    Öğe
    An adaptive noise canceller based on QLMS algorithm for removing EOG artifacts in EEG recordings
    (Ieee, 2017) Menguc, Engin Cemal; Acır, Nurettin
    In this paper, a novel adaptive noise canceller (ANC) based on the quaternion valued least mean square algorithm (QLMS) is designed in order to remove electrooculography (EOG) artifacts from electroencephalography (EEG) recordings. The measurement real-valued EOG and EEG signals (FP1, FP2, AF3 and AF4) are first modeled as four-dimensional processes in the quaternion domain. The EOG artifacts are then removed from the EEG signals in the quaternion domain by using the ANC based on QLMS algorithm. The quaternion representation of these signals allows us to remove EOG artifacts from all channels at the same time instead of removing the EOG artifacts in each EEG recordings separately. The simulation results support the proposed approach.
  • Küçük Resim Yok
    Öğe
    An Augmented Complex-Valued Least-Mean Kurtosis Algorithm for the Filtering of Noncircular Signals
    (Ieee-Inst Electrical Electronics Engineers Inc, 2018) Menguc, Engin Cemal; Acır, Nurettin
    In this paper, a novel augmented complex-valued least-mean kurtosis (ACLMK) algorithm is proposed for processing complex-valued signals. The negated kurtosis of the complex-valued error signal is defined as a cost function by using augmented statistics. As a result of the minimization of this cost function, the ACLMK algorithm containing all second-order statistical properties is obtained for processing noncircular complex-valued signals. Moreover, in this paper, convergence and misadjustment conditions of the proposed ACLMK algorithm are derived from the steady-state analysis. The simulation results on complex-valued system identification, prediction, and adaptive noise cancelling problems show that the use of the cost function defined by the negated kurtosis of the complex-valued error signal based on augmented statistics enables the processing of the noncircular complex-valued signals, and significantly improves the performance of the proposed ACLMK algorithm in terms of the mean square deviation, the mean square error, the prediction gain and the convergence rate when compared to other algorithms.
  • Küçük Resim Yok
    Öğe
    An augmented complex-valued Lyapunov stability theory based adaptive filter algorithm
    (Elsevier, 2017) Menguc, Engin Cemal; Acır, Nurettin
    A novel augmented complex-valued Lyapunov stability theory (LST) based adaptive filter (ACLAF) algorithm is proposed for the widely linear adaptive filtering of noncircular complex-valued signals. After a candidate Lyapunov function is determined, the design procedure is formulated as an inequality constrained optimization problem by using augmented statistics and LST. Thus, the proposed algorithm has improved the adaptive filtering of noncircular complex-valued signals by a unified framework of the LST and augmented complex statistics. Moreover, we statistically show that the ACLAF algorithm converges to the optimal Wiener solution under stationary environments, the required condition of the step size for the stability of the ACLAF algorithm is obtained by convergence in mean analysis and a new approach. In addition, the variance of the ACLAF algorithm is statically analysed in this study. The performance of the ACLAF algorithm is tested on circular and noncircular benchmark signals and on a real-world non circular wind signal. Simulation results verify that the ACLAF algorithm outperforms complex-valued LST based adaptive filter (CLAF), complex-valued least mean square (CLMS), complex-valued normalized least mean square (CNLMS), augmented CLMS (ACLMS) and augmented CNLMS (ACNLMS) algorithms for adaptive prediction of noncircular signals in terms of prediction gain, convergence rate and mean square error (MSE). Also, the ACLAF algorithm enhances the prediction gain by more than 25% when compared to the other augmented algorithms. (C) 2017 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    Complex-Valued Least Mean Kurtosis Adaptive Filter Algorithm
    (Ieee, 2016) Menguc, Engin Cemal; Acır, Nurettin
    In this study, a complex-valued least mean Kurtosis (CLMK) adaptive filter algorithm is designed for processing complex-valued signals. The performance of the designed algorithm is tested on a complex-valued system identification and compared the complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) algorithms. As a result, the CLMK algorithm shows a higher performance than the other algorithms in terms of the convergence rate, mean square error (MSE) and mean square deviation (MSD).
  • Küçük Resim Yok
    Öğe
    A generalized Lyapunov stability theory-based adaptive FIR filter algorithm with variable step sizes
    (Springer London Ltd, 2017) Menguc, Engin Cemal; Acır, Nurettin
    This paper presents a novel approach to Lyapunov stability theory-based adaptive filter (LAF) design. The proposed design is based on the minimization of the Euclidean norm of the difference weight vector under negative definiteness constraint defined over a novel linear Lyapunov function. The proposed fixed step size LAF (FSS-LAF) algorithm is first obtained by using the method of Lagrangian multipliers. The FSS-LAF satisfying asymptotic stability in the sense of Lyapunov provides a significant performance gain in the presence of a measurement noise. The stability of the FSS-LAF algorithm is also statistically analyzed in this study. Moreover, gradient variable step size (VSS) algorithms are adapted to the FSS-LAF algorithm to further enhance the performance for the first time in this paper. These VSS algorithms are Benveniste (BVSS), Mathews and Farhang-Ang (FVSS) algorithms. Simulation results on system identification problems show that the bounds of step size for the FSS-LAF algorithm are verified, and especially, the BVSS-LAF and FVSS-LAF algorithms provide a better trade-off between steady-state mean square deviation error and convergence rate than other proposed algorithms.
  • Küçük Resim Yok
    Öğe
    Kurtosis-Based CRTRL Algorithms for Fully Connected Recurrent Neural Networks
    (Ieee-Inst Electrical Electronics Engineers Inc, 2018) Menguc, Engin Cemal; Acır, Nurettin
    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.
  • Küçük Resim
    Öğe
    LYAPUNOV THEORY BASED ADAPTIVE LEARNING ALGORITHM FOR MULTILAYER NEURAL NETWORKS
    (Acad Sciences Czech Republic, Inst Computer Science, 2014) Acır, Nurettin; Menguc, Engin Cemal
    This paper presents a novel weight updating algorithm for training of multilayer neural network (MLNN). The MLNN system is first linearized and then the design procedure is proposed as an inequality constraint optimization problem. A well selected Lyapunov function is suitably determined and integrated into the constraint function for satisfying asymptotic stability in the sense of Lyapunov. Thus, the convergence capability of training algorithm is improved by using a new analytical adaptation gain rate which has the ability to adaptively adjust itself depending on a sequential square error rate. The proposed algorithm is compared with two types of backpropagation algorithms and a Lyapunov theory based MLNN algorithm on three benchmark problems which are XOR, 3-bit parity, and 8-3 encoder. The results are compared in terms of number of learning iterations and computational time required for a specified convergence rate. The results clearly indicate that the proposed algorithm is much faster in convergence than other three algorithms. The proposed algorithm is also comparatively tested on a real iris image database for multiple-input and multiple-output classification problem and the effect of adaptation gain rate for faster convergence and higher performance is verified.
  • Küçük Resim Yok
    Öğe
    A New Approach to Channel Equalization Problem
    (Ieee, 2015) Menguc, Engin Cemal; Acır, Nurettin
    In this study, a new approach based on Lyapunov stability theory (LST) is proposed for channel equalization problem. For the first time, the convergence capability of the proposed algorithm is presented on the channel equalization problem. The proposed approach is compared with normalized least mean square (NLMS) algorithm. Simulation results show that the convergence capability of the proposed algorithm is better than NLMS algorithm. As a result, the proposed approach can effectively be used for the channel equalization problem.
  • Küçük Resim
    Öğe
    A novel adaptive filter design using Lyapunov stability theory
    (Tubitak Scientific & Technical Research Council Turkey, 2015) Menguc, Engin Cemal; Acır, Nurettin
    This paper presents a new approach to design an adaptive filter using Lyapunov stability theory. The design procedure is formulated as an inequality constrained optimization problem. Lagrange multiplier theory is used as an optimization tool. Lyapunov stability theory is integrated into the constraint function to satisfy the asymptotic stability of the proposed filtering system. The tracking capability is improved by using a new analytical adaptation gain rate, which has the ability to adaptively adjust itself depending on a sequential tracking square error rate. The fast and robust convergence ability of the proposed algorithm is comparatively examined by simulation examples.
  • Küçük Resim Yok
    Öğe
    Prediction of Complex-Valued Signals by Using Complex-Valued LMK Algorithm
    (Ieee, 2017) Menguc, Engin Cemal; Acır, Nurettin
    In this study, the adaptive prediction of complex valued signals has been realized by using the complex-valued least mean Kurtosis (CLMK) algorithm. The prediction performance of the CLMK algorithm has been evaluated on three benchmark complex-valued signals and a complex-valued real-world radar data by comparing with the traditional least mean square (CLMS) algorithm. Simulation results have verified that the CLMK algorithm outperforms the CLMS algorithm.
  • Küçük Resim Yok
    Öğe
    Real-Time Implementation of Lyapunov Stability Theory-Based Adaptive Filter on FPGA
    (Ieice-Inst Electronics Information Communications Eng, 2016) Menguc, Engin Cemal; Acır, Nurettin
    The Lyapunov stability theory-based adaptive filter (LST-AF) is a robust filtering algorithm which the tracking error quickly converges to zero asymptotically. Recently, the software module of the LST-AF algorithm is effectively used in engineering applications such as tracking, prediction, noise cancellation and system identification problems. Therefore, hardware implementation becomes necessary in many cases where real time procedure is needed. In this paper, an implementation of the LST-AF algorithm on Field Programmable Gate Arrays (FPGA) is realized for the first time to our knowledge. The proposed hardware implementation on FPGA is performed for two main benchmark problems; i) tracking of an artificial signal and a Henon chaotic signal, ii) estimation of filter parameters using a system identification model. Experimental results are comparatively presented to test accuracy, performance and logic occupation. The results show that our proposed hardware implementation not only conserves the capabilities of software versions of the LST-AF algorithm but also achieves a better performance than them.