Dr. Morteza Pakdaman

Atmospheric Science and Meteorological Research Center (ASMERC)

Climatological Research Center (CRI), Mashhad, Iran

Assistant Professor



2014 Ph.D., Ferdowsi University of Mashhad, Mashahd, Iran

Publications (Selected)

  1. 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.
  2. A continuous-time optimal control model for workforce planning considering human resource strategies (HRS), A Pooya, M Pakdaman, SM Ebrahimpour, Kybernetes.
  3. 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.
  4. Extreme Climate Events in Iran during 2018, L Khazanedari, S Malbosi, S Samadi Neghab, M Pakdaman, Z Javanshiri, Nivar 44 (108-109), 68-78.
  5. 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.
  6. Design of optimization model and Decision Support System to determine the capacity of a number of types of public transportation of urban bus lines, A Pooya, M Pakdaman, S Fadaei, M Chaichi Motlagh, S Sadraei, Journal of Transportation Research.
  7. Optimal control model for finite capacity continuous MRP with deteriorating items A Pooya, M Pakdaman, Journal of Intelligent Manufacturing 30 (5), 2203-2215.
  8. 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 (2), 333-346.
  9. A new continuous time optimal control model for manpower planning with promotion from inside the system, A Pooya, M Pakdaman, Operational Research, 1-16.
  10. A delayed optimal control model for multi-stage production-inventory system with production lead times, A Pooya, M Pakdaman,The International Journal of Advanced Manufacturing Technology 94 (1), 751-761.
  11. Analysing the solution of production-inventory optimal control systems by neural networks A Pooya, M Pakdaman, RAIRO-Operations Research 51 (3), 577-590.
  12. A neural network approach for solving a class of fractional optimal control problems J Sabouri, S Effati, M Pakdaman, Neural Processing Letters 45 (1), 59-74.
  13. 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.
  14. Exact And Approximate Solution Of A Two-Stock Inventory System With Forecasting Of Demand And Return Rates, A Pooya, M Pakdaman, L Tadj, 2017International Academic Conference on Business.
  15. Bounds for convex quadratic programming problems and some important applications M Pakdaman, S Effati, International Journal of Operational Research 30 (2), 277-287.
  16. On fuzzy linear projection equation and applications, M Pakdaman, S Effati Fuzzy Optimization and Decision Making 15 (2), 219-236.
  17. Approximating the solution of optimal control problems by fuzzy systems, M Pakdaman, S Effati, Neural Processing Letters 43 (3), 667-686.
  18. Fuzzy projection over a crisp set and applications, M Pakdaman, S Effati, International Journal of Fuzzy Systems 18 (2), 312-319.
  19. Optimal control problem via neural networks, S Effati, M Pakdaman, Neural Computing and Applications 23 (7), 2093-2100.
  20. Ordinary differential equations solution in kernel space, HS Yazdi, H Modaghegh, M Pakdaman, Neural Computing and Applications 21 (1), 79-85.
  21. Solving the interval-valued linear fractional programming problem, S Effati, M Pakdaman Scientific Research Publishing.
  22. Unsupervised kernel least mean square algorithm for solving ordinary differential equations HS Yazdi, M Pakdaman, H Modaghegh, Neurocomputing 74 (12-13), 2062-2071.
  23. Artificial neural network approach for solving fuzzy differential equations S Effati, M Pakdaman, Information Sciences 180 (8), 1434-1457.
  24. Fuzzy circuit analysis, HS Yazdi, M Pakdaman, S Effati, International Journal of Applied Engineering Research 3 (8), 1061-1072.