Yazar "Inan, Mevlut" seçeneğine göre listele

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    Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks
    (Ieee, 2019) Seyyarer, Ebubekir; Uckan, Taner; Hark, Cengiz; Ayata, Faruk; Inan, Mevlut; Karci, Ali
    Nowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field In this study, Convolutional Neural Network (CNN) model from the Deep Learning Algorithms and the Optimization Algorithms used in Deep Learning have been applied to international image data sets. Optimization algorithms were applied to both datasets respectively, the results were analyzed and compared The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.
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    Investigation of PIDA Controller Parameters via PSO Algorithm
    (Ieee, 2018) Donuk, Kenan; Ozbey, Necati; Inan, Mevlut; Yeroglu, Celaleddin; Hanbay, Davut
    In this study, a method is proposed to determine the parameters of Proportional Integral Derivative Acceleration (PIDA) controller, which are used in the control of higher degree systems, via Particle Swarm Optimization (PSO) algorithm. Classic controller methods may sometimes be insufficient, especially in the control of higher degree systems. In PIDA controller design, heuristic optimization algorithms, which is one of the recommended methods for these systems, provides successful results. The performance of the PIDA controller obtained by the proposed method in the paper is compared with the gradient search and genetic algorithms in the literature. The unit step response and the frequency response of the controller show the effectiveness of the method.
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    Performance Comparisons of Optimization Algorithms
    (Ieee, 2018) Inan, Mevlut; Karaduman, Mucahit; Karci, Ali
    Optimization methods are applied to many different problems. While these methods do not guarantee a definite end result, they give a solution that is close to the best result in a reasonable time.Optimization methods are classified as physical, social, music, herd, chemistry, biology and hybrid methods when classified according to the sources they are influenced by.In this study, it is aimed to compare the 5 methods of swarm optimization algorithm methods under the same conditions and applying the same probing.Thus, it is possible to determine the method that obtains the best values in terms of result and speed, and gives the fastest result. For this purpose, cat swarm optimization, whale swarm optimization, cricket algorithm, crow search optimization and salp optimization methods have been determined. When the result obtained from the comparison is evaluated, the best calculation time of the calculations for all functions is done with crow search optimization, the best results are obtained with whale swarm optimization for Ackley, salp optimization methods for Bukin N 6 and crow search optimization for Rastrigin.