Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Modeling brain wave data by using artificial neural networks
AU - Aladag, Cagdas Hakan
AU - Egrioglu, Erol
AU - Kadilar, Cem
PY - 2010/4/23
Y1 - 2010/4/23
N2 - Artificial neural networks can successfully model time series in real life. Because of their success, they have been widely used in various fields of application. In this paper, artificial neural networks are used to model brain wave data which has been recorded during the Wisconsin Card Sorting Test. The forecasting performances of different artificial neural network models, such as feed forward and recurrent neural networks, using both linear and nonlinear activation functions in the output neuron, are examined. As a result of the analysis, it is found that artificial neural networks model the data successfully and all the models employed produce very accurate forecasts.
AB - Artificial neural networks can successfully model time series in real life. Because of their success, they have been widely used in various fields of application. In this paper, artificial neural networks are used to model brain wave data which has been recorded during the Wisconsin Card Sorting Test. The forecasting performances of different artificial neural network models, such as feed forward and recurrent neural networks, using both linear and nonlinear activation functions in the output neuron, are examined. As a result of the analysis, it is found that artificial neural networks model the data successfully and all the models employed produce very accurate forecasts.
KW - Activation function
KW - Brain wave data
KW - Elman recurrent neural networks
KW - Feed forward neural networks
KW - Forecasting
KW - Wisconsin card sorting test
M3 - Journal article
AN - SCOPUS:77951090057
VL - 39
SP - 81
EP - 88
JO - Hacettepe Journal of Mathematics and Statistics
JF - Hacettepe Journal of Mathematics and Statistics
SN - 1303-5010
IS - 1
ER -