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Öğe The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey(Elsevier Science Sa, 2014) Ceylan, Ilhan; Gedik, Engin; Erkaymaz, Okan; Gurel, Ali EtemArtificial neural network (ANN) is a useful tool that using estimates behavior of the most of engineering applications. In the present study, ANN model has been used to estimate the temperature, efficiency and power of the Photovoltaic module according to outlet air temperature and solar radiation. An experimental system consisted photovoltaic module, heating and cooling sub systems, proportional integral derivative (PID) control unit was designed and built. Tests were realized at the outdoors for the constant ambient air temperatures of photovoltaic module. To preserve ambient air temperature at the determined constant values as 10, 20, 30 and 40 degrees C, cooling and heating subsystems which connected PID control unit were used in the test apparatus. Ambient air temperature, solar radiation, back surface of the photovoltaic module temperature was measured in the experiments. Obtained data were used to estimate the photovoltaic module temperature, efficiency and power with using ANN approach for all 7 region of the Turkey. The study dealing with this paper not only will beneficial for the limited region but also in all region of Turkey which will be thought established of photovoltaic panels by the manufacturer, researchers and etc. (C) 2014 Elsevier B.V. All rights reserved.Öğe Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems(2014) Erkaymaz, Okan; Özer, Mahmut; Yumusak, NejatSince feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.Öğe Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems(Tubitak Scientific & Technological Research Council Turkey, 2014) Erkaymaz, Okan; Ozer, Mahmut; Yumusak, NejatSince feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.Öğe Impact of synaptic noise and conductance state on spontaneous cortical firing(Lippincott Williams & Wilkins, 2007) Ozer, Mahmut; Graham, Lyle J.; Erkaymaz, Okan; Uzuntarla, MuhammetCortical neurons in-vivo operate in a continuum of overall conductance states, depending on the average level of background synaptic input throughout the dendritic tree. We compare how variability, or fluctuations, in this input affects the statistics of the resulting 'spontaneous' or 'background' firing activity, between two extremes of the mean input corresponding to a low-conductance (LC) and a high-conductance (HC) state. In the HC state, we show that both firing rate and regularity increase with increasing variability. In the LC state, firing rate also increases with input variability, but in contrast to the HC state, firing regularity first decreases and then increases with an increase in the variability. At high levels of input variability, firing regularity in both states converge to similar values.Öğe Modeling of Compressive Strength Parallel to Grain of Heat Treated Scotch Pine (Pinus sylvestris L.) Wood by Using Artificial Neural Network(Zagreb Univ, Fac Forestry, 2015) Yapici, Fatih; Esen, Rasit; Erkaymaz, Okan; Bas, HasanIn this study, the compressive strength of heat treated Scotch Pine was modeled using artificial neural network. The compressive strength (CS) value parallel to grain was determined after exposing the wood to heat treatment at temperature of 130, 145, 160, 175, 190 and 205 degrees C for 3, 6, 9, 12 hours. The experimental data was evaluated by using multiple variance analysis. Secondly, the effect of heat treatment on the CS of samples was modeled by using artificial neural network (ANN).Öğe Performance Analysis of A Feed-Forward Artifical Neural Network With Small-World Topology(Elsevier Science Bv, 2012) Erkaymaz, Okan; Ozer, Mahmut; Yumusak, NejatFeed Forward Artificial Neural Networks are the most widely used models to explain the information processing mechanism of the brain. Network topology plays a key role in the performance of the feed forward neural networks. Recently, the small-world network topology has been shown to meet the properties of the real life networks. Therefore, in this study, we consider a feed forward artificial neural network with small-world topology and analyze its performance on classifying the epilepsy. In order to obtain the small-world network, we follow the Watts-Strogatz approach. An EEG dataset taken from healthy and epileptic patients is used to test the performance of the network. We also consider different numbers of neurons in each layer of the network. By comparing the performance of small-world and regular feed forward artificial neural networks, it is shown that the Watts-Strogatz small-world network topology improves the learning performance and decreases the training time. To our knowledge, this is the first attempt to use small-world topology in a feed forward artificial neural network to classify the epileptic case.Öğe The prediction of photovoltaic module temperature with artificial neural networks(Elsevier, 2014) Ceylan, Ilhan; Erkaymaz, Okan; Gedik, Engin; Gurel, Ali EtemIn this study, photovoltaic module temperature has been predicted according to outlet air temperature and solar radiation. For this investigation, photovoltaic module temperatures have been determined in the experimental system for 10, 20, 30, and 40 degrees C ambient air temperature and different solar radiations. This experimental study was made in open air and solar radiation was measured and then this measured data was used for the training of ANN, Photovoltaic module temperatures have been predicted according to solar radiation and outside air temperature for the Aegean region in Turkey. Electrical efficiency and power was also calculated depending on the predicted module temperature. Kutahya, L4ak and Afyon are the most suitable cities in terms of electrical efficiency and power product in the Aegean region in Turkey. (C) 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY licenseÖğe Prediction of withdrawal strength of nail of uludag fir wood by using artificial neural network (anns)(2012) Yapıcı, Fatih; EŞen, Rasit; Kurt, Seref; Lıkos, Erkan; Erkaymaz, OkanIn this study, the effects of type of nails material and grain angle of wood on the withdrawal strength of nailhave been researched. For this purpose specimens were firstly cut in different sections from Uludağ Fir (Abiesbornmülleriana M.) wood. The tests of static nail strength were carried out according to the standards of TS EN13446. Secondly, an artificial neural network system was built by using data obtained in an experimental studyfor the prediction of withdrawal nail strength. The comparison between the experimental data and predicted datawas also carried outÖğe Prediction of Withdrawal Strength of Nail of Uludag Fir Wood by Using Artificial Neural Network (ANNs)(Kastamonu Univ, Orman Fak, 2012) Yapici, Fatih; Esen, Rasit; Kurt, Seref; Likos, Erkan; Erkaymaz, OkanIn this study, the effects of type of nails material and grain angle of wood on the withdrawal strength of nail have been researched. For this purpose specimens were firstly cut in different sections from Uludag Fir (Abies bornmulleriana M.) wood. The tests of static nail strength were carried out according to the standards of TS EN 13446. Secondly, an artificial neural network system was built by using data obtained in an experimental study for the prediction of withdrawal nail strength. The comparison between the experimental data and predicted data was also carried out.
