| 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 | 105 | 2015 |
| A modified genetic algorithm for forecasting fuzzy time series E Bas, VR Uslu, U Yolcu, E Egrioglu Applied intelligence 41, 453-463, 2014 | 84 | 2014 |
| High order fuzzy time series method based on pi-sigma neural network E Bas, C Grosan, E Egrioglu, U Yolcu Engineering Applications of Artificial Intelligence 72, 350-356, 2018 | 69 | 2018 |
| A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations VR Uslu, E Bas, U Yolcu, E Egrioglu Swarm and Evolutionary Computation 15, 19-26, 2014 | 69 | 2014 |
| Intuitionistic fuzzy time series functions approach for time series forecasting E Bas, U Yolcu, E Egrioglu Granular Computing 6 (3), 619-629, 2021 | 67 | 2021 |
| The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting E Bas Journal of Artificial Intelligence and Soft Computing Research 6 (1), 5-11, 2016 | 64 | 2016 |
| 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 | 64 | 2015 |
| Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony E Egrioglu, U Yolcu, E Bas Granular Computing 4, 639-654, 2019 | 58 | 2019 |
| Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization E Bas, E Egrioglu, E Kolemen Granular Computing 7 (2), 411-420, 2022 | 55 | 2022 |
| Robust learning algorithm for multiplicative neuron model artificial neural networks E Bas, VR Uslu, E Egrioglu Expert Systems with Applications 56, 80-88, 2016 | 55 | 2016 |
| An ARMA type pi-sigma artificial neural network for nonlinear time series forecasting E Akdeniz, E Egrioglu, E Bas, U Yolcu Journal of Artificial Intelligence and Soft Computing Research 8 (2), 121-132, 2018 | 42 | 2018 |
| Picture fuzzy time series: Defining, modeling and creating a new forecasting method E Egrioglu, E Bas, U Yolcu, MY Chen Engineering Applications of Artificial Intelligence 88, 103367, 2020 | 41 | 2020 |
| Median-Pi artificial neural network for forecasting E Egrioglu, U Yolcu, E Bas, AZ Dalar Neural Computing and Applications 31, 307-316, 2019 | 41 | 2019 |
| Fuzzy lagged variable selection in fuzzy time series with genetic algorithms CH Aladag, U Yolcu, E Egrioglu, E Bas Applied Soft Computing 22, 465-473, 2014 | 37 | 2014 |
| Recurrent fuzzy time series functions approaches for forecasting E Egrioglu, R Fildes, E Baş Granular Computing, 1-8, 2022 | 33 | 2022 |
| Probabilistic fuzzy time series method based on artificial neural network E Egrioglu, E Bas, CH Aladag, U Yolcu American Journal of Intelligent Systems 62 (2), 42-47, 2016 | 32 | 2016 |
| Type 1 fuzzy function approach based on ridge regression for forecasting E Bas, E Egrioglu, U Yolcu, C Grosan Granular Computing 4, 629-637, 2019 | 30 | 2019 |
| The training of pi-sigma artificial neural networks with differential evolution algorithm for forecasting O Yılmaz, E Bas, E Egrioglu Computational Economics 59 (4), 1699-1711, 2022 | 29 | 2022 |
| A new adaptive network based fuzzy inference system for time series forecasting E Egrioglu, CH Aladag, U Yolcu, E Bas Aloy J Soft Comput Appl 2 (1), 25-32, 2014 | 28 | 2014 |
| Recurrent dendritic neuron model artificial neural network for time series forecasting E Egrioglu, E Baş, MY Chen Information Sciences 607, 572-584, 2022 | 27 | 2022 |
Erol EgriogluGiresun University, Department of Statistics在 giresun.edu.tr 的电子邮件经过验证
