| Solving differential equations of fractional order using an optimization technique based on training artificial neural network M Pakdaman, A Ahmadian, S Effati, S Salahshour, D Baleanu Applied Mathematics and Computation 293, 81-95, 2017 | 153 | 2017 |
| Artificial neural network approach for solving fuzzy differential equations S Effati, M Pakdaman Information Sciences 180 (8), 1434-1457, 2010 | 153 | 2010 |
| A neural network approach for solving a class of fractional optimal control problems J Sabouri, S Effati, M Pakdaman Neural Processing Letters 45, 59-74, 2017 | 99 | 2017 |
| Optimal control problem via neural networks S Effati, M Pakdaman Neural Computing and Applications 23, 2093-2100, 2013 | 74 | 2013 |
| Unsupervised kernel least mean square algorithm for solving ordinary differential equations HS Yazdi, M Pakdaman, H Modaghegh Neurocomputing 74 (12-13), 2062-2071, 2011 | 62 | 2011 |
| Solving the interval-valued linear fractional programming problem S Effati, M Pakdaman Scientific Research Publishing, 2012 | 56 | 2012 |
| Approximating the solution of optimal control problems by fuzzy systems M Pakdaman, S Effati Neural Processing Letters 43, 667-686, 2016 | 24 | 2016 |
| Optimal control model for finite capacity continuous MRP with deteriorating items A Pooya, M Pakdaman Journal of Intelligent Manufacturing 30, 2203-2215, 2019 | 23 | 2019 |
| Lightning prediction using an ensemble learning approach for northeast of Iran M Pakdaman, SS Naghab, L Khazanedari, S Malbousi, Y Falamarzi Journal of Atmospheric and Solar-Terrestrial Physics 209, 105417, 2020 | 22 | 2020 |
| A new fuzzy neural network model for solving fuzzy linear programming problems and its applications S Effati, M Pakdaman, M Ranjbar Neural Computing and Applications 20, 1285-1294, 2011 | 22 | 2011 |
| Exact and approximate solution for optimal inventory control of two-stock with reworking and forecasting of demand A Pooya, M Pakdaman, L Tadj Operational Research 19, 333-346, 2019 | 21 | 2019 |
| Post-processing of the North American multi-model ensemble for monthly forecast of precipitation based on neural network models M Pakdaman, Y Falamarzi, I Babaeian, Z Javanshiri | 20 | 2020 |
| A kernel least mean square algorithm for fuzzy differential equations and its application in earth’s energy balance model and climate M Pakdaman, Y Falamarzi, HS Yazdi, A Ahmadian, S Salahshour, ... Alexandria Engineering Journal 59 (4), 2803-2810, 2020 | 14 | 2020 |
| Fuzzy circuit analysis HS Yazdi, M Pakdaman, S Effati International journal of applied engineering research 3 (8), 1061-1072, 2008 | 12 | 2008 |
| A new iterative technique for solving fractal-fractional differential equations based on artificial neural network in the new generalized Caputo sense AM Shloof, N Senu, A Ahmadian, M Pakdaman, S Salahshour Engineering with Computers 39 (1), 505-515, 2023 | 10 | 2023 |
| A new continuous time optimal control model for manpower planning with promotion from inside the system A Pooya, M Pakdaman Operational Research 21 (1), 349-364, 2021 | 10 | 2021 |
| Analysing the solution of production-inventory optimal control systems by neural networks A Pooya, M Pakdaman RAIRO-Operations Research-Recherche Opérationnelle 51 (3), 577-590, 2017 | 10 | 2017 |
| Ordinary differential equations solution in kernel space H Sadoghi Yazdi, H Modaghegh, M Pakdaman Neural Computing and Applications 21, 79-85, 2012 | 10 | 2012 |
| European multi model ensemble (EMME): A new approach for monthly forecast of precipitation M Pakdaman, I Babaeian, Z Javanshiri, Y Falamarzi Water Resources Management 36 (2), 611-623, 2022 | 9 | 2022 |
| Homogenization and trend detection of temperature in Iran for the period 1960–2018 Z Javanshiri, M Pakdaman, Y Falamarzi Meteorology and Atmospheric Physics 133, 1233-1250, 2021 | 8 | 2021 |
Sohrab EffatiProfessor of Applied Mathematics, Ferdowsi University of MashhadVerified email at um.ac.ir
