Dr. Morteza Pakdaman

Atmospheric Science and Meteorological Research Center (ASMERC)

Climatological Research Center (CRI), Mashhad, Iran

Assistant Professor

Email: pakdaman.m@gmail.com

Qualifications

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.