| Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations CH Aladag, MA Basaran, E Egrioglu, U Yolcu, VR Uslu Expert Systems with Applications 36 (3), 4228-4231, 2009 | 252 | 2009 |
| Forecasting nonlinear time series with a hybrid methodology CH Aladag, E Egrioglu, C Kadilar Applied Mathematics Letters 22 (9), 1467-1470, 2009 | 209 | 2009 |
| Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks E Egrioglu, CH Aladag, U Yolcu Expert Systems with Applications 40 (3), 854-857, 2013 | 192 | 2013 |
| A new linear & nonlinear artificial neural network model for time series forecasting U Yolcu, E Egrioglu, CH Aladag Decision support systems 54 (3), 1340-1347, 2013 | 172 | 2013 |
| A new approach for determining the length of intervals for fuzzy time series U Yolcu, E Egrioglu, VR Uslu, MA Basaran, CH Aladag Applied soft computing 9 (2), 647-651, 2009 | 166 | 2009 |
| A new approach based on artificial neural networks for high order multivariate fuzzy time series E Egrioglu, CH Aladag, U Yolcu, VR Uslu, MA Basaran Expert Systems with Applications 36 (7), 10589-10594, 2009 | 162 | 2009 |
| A new time invariant fuzzy time series forecasting method based on particle swarm optimization CH Aladag, U Yolcu, E Egrioglu, AZ Dalar Applied Soft Computing 12 (10), 3291-3299, 2012 | 154 | 2012 |
| Finding an optimal interval length in high order fuzzy time series E Egrioglu, CH Aladag, U Yolcu, VR Uslu, MA Basaran Expert Systems with Applications 37 (7), 5052-5055, 2010 | 151 | 2010 |
| A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model E Egrioglu, CH Aladag, U Yolcu, MA Basaran, VR Uslu Expert Systems with Applications 36 (4), 7424-7434, 2009 | 128 | 2009 |
| A new model selection strategy in artificial neural networks E Eğrioğlu, ÇH Aladağ, S Günay Applied Mathematics and Computation 195 (2), 591-597, 2008 | 127 | 2008 |
| A new approach based on the optimization of the length of intervals in fuzzy time series E Egrioglu, CH Aladag, MA Basaran, U Yolcu, VR Uslu Journal of Intelligent & Fuzzy Systems 22 (1), 15-19, 2011 | 123 | 2011 |
| A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks CH Aladag, U Yolcu, E Egrioglu Mathematics and Computers in Simulation 81 (4), 875-882, 2010 | 121 | 2010 |
| Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering E Egrioglu, CH Aladag, U Yolcu, VR Uslu, NA Erilli Expert Systems with Applications 38 (8), 10355-10357, 2011 | 119 | 2011 |
| Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting E Egrioglu, U Yolcu, CH Aladag, E Bas Neural Processing Letters 41, 249-258, 2015 | 112 | 2015 |
| The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem CH Aladag, G Hocaoglu, MA Basaran Expert systems with applications 36 (10), 12349-12356, 2009 | 109 | 2009 |
| Using multiplicative neuron model to establish fuzzy logic relationships CH Aladag Expert systems with applications 40 (3), 850-853, 2013 | 83 | 2013 |
| A tabu search algorithm to solve a course timetabling problem ÇH Aladağ, G Hocaoğlu Hacettepe journal of mathematics and statistics 36 (1), 53-64, 2007 | 73 | 2007 |
| Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market U Yolcu, CH Aladag, E Egrioglu, VR Uslu Journal of Statistical Computation and Simulation 83 (4), 599-612, 2013 | 72 | 2013 |
| Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks NA Erilli, U Yolcu, E Eğrioğlu, ÇH Aladağ, Y Öner Expert Systems with Applications 38 (3), 2248-2252, 2011 | 67 | 2011 |
| Fuzzy-time-series network used to forecast linear and nonlinear time series E Bas, E Egrioglu, CH Aladag, U Yolcu Applied Intelligence 43, 343-355, 2015 | 66 | 2015 |
Prof. Dr. CEM KADILARHacettepe UniversityVahvistettu sähköpostiosoite verkkotunnuksessa hacettepe.edu.tr