Filtreler
Filtreler
Bulunan: 9 Adet 0.000 sn
Koleksiyon [11]
Tam Metin [2]
Yayın Türü [1]
Yazar [20]
Yayın Yılı [4]
Konu Başlıkları [20]
Yayıncı [1]
Yayın Dili [1]
Dergi Adı [1]
Determining of stock investments with grey relational analysis

Hamzaçebi C. | Pekkaya M.

Article | 2011 | Expert Systems with Applications38 ( 8 ) , pp.9186 - 9195

Selecting stock is important problem for investors. Investors can use related financial ratios in stock selection. These kind of worthy financial ratios can be obtained from financial statements. The investors can use these ratios as criteria while they are selecting the stocks. Since dealing with more than one financial ratio, the investing issue becomes multi-criteria decision making (MCDM) problem for the investors. There are various techniques for solving MCDM problems in literature. In this study grey relational analysis (GRA) is used for ordering some financial firms' stocks which are in Financial Sector Index of Istanbul Stoc . . .k Exchange (ISE). Besides, because of the importance of criteria weights in decision making, three different approaches - heuristic, Analytic Hierarchy Process, learning via sample - were experimented to find best values of criteria weights in GRA process. © 2011 Published by Elsevier Ltd Daha fazlası Daha az

Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

Hamzaçebi C. | Akay D. | Kutay F.

Article | 2009 | Expert Systems with Applications36 ( 2 PART 2 ) , pp.3839 - 3844

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multi-periodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in o . . .rder to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result. © 2008 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Parameter estimation in mathematical models using the real coded genetic algorithms

Tutkun N.

Article | 2009 | Expert Systems with Applications36 ( 2 PART 2 ) , pp.3342 - 3345

In this study, parameter estimation in mathematical models using the real coded genetic algorithms (RCGA) approach is presented. Although the RCGA is similar with the binary coded genetic algorithms (BCGA) in terms of genetic process, it has few advantages such as high precision, non-existence of Hamming's cliff etc., over the BCGA. In this approach, creating initial population and selection procedure are almost the same with the BCGA, but crossover and mutation operations. The proposed approach is implemented on the second order ordinary differential equations modeling the enzyme effusion problem and it is compared with previous ap . . .proaches. The results indicate that the proposed approach produced better estimated results with respect to previous findings. © 2008 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Comparison of different matching methods in observational studies and sensitivity analysis: The relation between depression and STAI-2 scores

Ankarali H.C. | Sumbuloglu V. | Yazici A.C. | Yalug I. | Selekler M.

Article | 2009 | Expert Systems with Applications36 ( 2 PART 1 ) , pp.1876 - 1884

In researches where two or more groups are desired to be compared, observational and randomized experiments are very frequently used. As the subjects are randomly assigned to the groups in randomized experiments, balance is provided in observed/unobserved covariates of subjects in different groups. As the subjects cannot be randomly distributed into groups in observational studies, balance of observed/unobserved covariates between groups is not provided. This situation causes a biased estimate of the treatment effect. In this research, it is focused on different matching methods in observational studies and elimination of observed c . . .ovariate effects confounding in the group effect, and these methods are examined comparatively. For this purpose, the effect of depression in 300 migraine patients, obtained from an observational study, on State continuous anxiety scale scores is taken and compared with the five different matching methods. Sensitivity of results is examined and it is researched whether the effect of treatment contains any bias. When results are examined, it is seen that matching methods produce similar results due to the overlap of propensity distribution in groups, high and balanced number of subjects in groups and covariates being not so many in number. The effects of unobserved covariates do not change the effect of treatment significantly. In conclusion, it is seen that, in the estimation of group effect in observational studies, it is possible to eliminate the effects of observed covariates using matching methods and matching quality of matching methods based on the propensity score is high. © 2007 Elsevier Ltd. All rights reserved Daha fazlası Daha az

EEG signals classification using the K-means clustering and a multilayer perceptron neural network model

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Article | 2011 | Expert Systems with Applications38 ( 10 ) , pp.13475 - 13481

We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of . . . healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates. © 2010 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Optimization of digital holographic setup by a fuzzy logic prediction system

Kaya, Gülhan Ustabaş | Erkaymaz, Okan | Saraç, Zehra

Article | 2016 | Expert Systems with Applications56 , pp.177 - 185

In this study, the optimization of the digital holography setup is achieved by a using fuzzy logic prediction system. In fact, when this optimization process is experimentally performed, some parameters are changed in the setup. These parameters affect directly the obtained image quality after a reconstruction process, which is determined by normalized root mean square. The aim of this study is to achieve the optimization of digital holographic setup by using both experimental and fuzzy logic prediction systems. Furthermore, the required time during the experimental optimization can be lowered by using a numerical method like the fu . . .zzy logic prediction system. Here, the experimental optimization results and the optimization results obtained by the fuzzy logic prediction system are compared. It is offered that the designed experimental system can be optimized by using an artificial intelligent tool. The applied fuzzy logic prediction model is used the first time for optimization of hologram recording setup. As a result, it is reached a conclusion that the optimization of digital holographic setup can be numerically performed by the fuzzy logic prediction system. Moreover, while digital holographic setup is experimentally designed, the required time for optimization is reduced, as well. © 2016 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Optimization of multimodal continuous functions using a new crossover for the real-coded genetic algorithms

Tutkun N.

Article | 2009 | Expert Systems with Applications36 ( 4 ) , pp.8172 - 8177

In this study, a new crossover approach to the real-coded genetic algorithm is proposed. The approach is simply based on efficiently tuned real-coded crossover operation using the probability distribution function of Gauss distribution to generate rather dissimilar strings which may be candidates of possible solutions. Also linear and quadratic mapping algorithms comparatively used both to constrain individuals in the given search spaces and to produce different individuals in order to increase average fitness relatively for the same population. Moreover, to refine genetically found optimum points the local search technique based on . . . Newton's method was performed. The designed software was first implemented on 11 well-known test functions and their results were compared with previous findings as shown in tables. In few test functions, the elitism operator was put into effect to maintain fitness stability helping increase the search performance of the proposed algorithm. The results indicate that the solutions to the test functions were almost the same with theoretical ones and the number of function evaluations for each test function was less than that obtained from using previous approaches. © 2008 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Improved power quality in a single-phase PWM inverter voltage with bipolar notches through the hybrid genetic algorithms

Tutkun N.

Article | 2010 | Expert Systems with Applications37 ( 8 ) , pp.5614 - 5620

Inverters are such electronic devices that are extensively used to convert DC power to AC power in wide-range domestic and industrial applications. DC power is usually supplied from either national grid through the rectifiers or batteries for high and low energy demands, respectively. When they are employed to feed electrical devices the efficiency of these devices tends to decrease due to harmonic occurrence in them. One solution to this problem is the selective harmonic elimination which is required to solve multiple non-linear equations simultaneously. These equations are mainly function of the switching angles and are obtained f . . .rom a type of inverter output voltage such as being unipolar, bipolar, etc. To eliminate one or more harmonics in these output voltages becomes a typical multivariate root-finding problem and its solution requires root-finding methods such as the Newton-Raphson method, the Broyden method etc. These methods can only produce a meaningful solution if initial values are properly guessed, but it is highly difficult task due to having no direct formula. In this paper, the hybrid genetic algorithm method based on the refinement of the genetic algorithms results through the Newton-Raphson method was used to simultaneously solve the non-linear equations. The results have shown a considerable effect on decrease in the total harmonic distortion, thereby increasing the efficiency of devices compared to conventional methods under the same conditions. © 2010 Elsevier Ltd. All rights reserved Daha fazlası Daha az

A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model

Çakmakçı, Mehmet | Kınacı, Cumali | Bayramoğlu, Mahmut | Yıldırım, Yılmaz

Article | 2010 | Expert Systems with Applications37 ( 2 ) , pp.1369 - 1373

Effluent iron concentration is an important water quality criterion used for the assessment of the performance of rapid sand filters, in addition to other criteria. This study deals with the prediction of effluent iron concentrations by adaptive neuro-fuzzy (ANFIS) model with input parameters including filter hydraulic loading rate, influent iron concentration, bed porosity and operation time. With trying various types of membership functions, two rule base generation methods, namely subtractive clustering and grid partition were used for a first order Sugeno type inference system. Models were evaluated using root mean squared error . . . (RMSE), index of agreement (IA) and R2 as statistical performance parameters. The fit between experimental results and model outputs showed good agreement for tap water and deionized water; testing RMSE values were 36.33 and 7.66 µg/L, the IA values were 0.996 and 0.971, and R2 values were 0.99 and 0.89, respectively. It was concluded that neuro-fuzzy modeling may be successfully used to predict effluent iron concentration in sand filtration. © 2009 Elsevier Ltd. All rights reserved Daha fazlası Daha az

6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms