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Effati, S., & Pakdaman, M. (2010). Artificial neural network approach for solving fuzzy differential equations. Information Sciences, 180(8), 1434-1457.
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Pakdaman, M., Ahmadian, A., Effati, S., Salahshour, S., & Baleanu, D. (2017). Solving differential equations of fractional order using an optimization technique
based on training artificial neural network. Applied Mathematics and Computation, 293, 81-95.
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Pakdaman, M., Nokhandan, M. H., & Falamarzi, Y. (2021). Revisiting albedo from a fuzzy perspective. Kybernetes.
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Pakdaman, M., Naghab, S. S., Khazanedari, L., Malbousi, S., & Falamarzi, Y. (2020). Lightning prediction using an ensemble learning approach for northeast of
Iran. Journal of Atmospheric and Solar-Terrestrial Physics, 209, 105417.
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Pakdaman, M., Falamarzi, Y., Yazdi, H. S., Ahmadian, A., Salahshour, S., & Ferrara, F. (2020). A kernel least mean square algorithm for fuzzy differential
equations and its application in earth’s energy balance model and climate. Alexandria Engineering Journal, 59(4), 2803-2810.
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Pakdaman, M., Falamarzi, Y., Babaeian, I., & Javanshiri, Z. (2020). Post-processing of the North American multi-model ensemble for monthly forecast of
precipitation based on neural network models. Theoretical and Applied Climatology, 141(1), 405-417.
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Pooya, A., & Pakdaman, M. (2019). Optimal control model for finite capacity continuous MRP with deteriorating items. Journal of Intelligent Manufacturing,
30(5), 2203-2215.
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Yazdi, H. S., Pakdaman, M., & Effati, S. (2008). Fuzzy circuit analysis. International Journal of Applied Engineering Research, 3(8), 1061-1072.
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Pooya, A., & Pakdaman, M. (2021). A new continuous time optimal control model for manpower planning with promotion from inside the system. Operational
Research, 21(1), 349-364.