Aghashariatmadari, Z., Khalili, A., Irannejad, P., Liyaghat, A. 2011. Calibration Annual Changes of the Coefficients of the Angstrom-Prescott (A-P) Equation (a and b) in Different Time Scale (Case study: Tehran North Station (Aghdasieh). Journal of Water and Soil, 25(4): 5-911. (in Farsi)
Almorox, J. Y., Hontoria, C. 2004. Global solar radiation estimation using sunshine duration in Spain. Energy Conversion and Management, 45(9-10): 1529-1535.
Angstrom, A. 1924. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society, 50(210): 121-126.
Daneshyar, M. (1978). Solar radiation statistics for Iran. Solar Energy; (United States), 21(4): 345-349.
Halabi, L., Mekhilef, S., Hossain, M. 2018. Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Applied energy, 213: 247-261.
Haykin, K. 1994. Neural network, A comprehensive foundation. MacMillan Press.
Hooshangi, N., Alesheikh, A. 2015. Evaluation of fuzzy, neural and fuzzy-neural methods in estimating solar radiation in the country. Scientific Research Journal of Surveying Science and Technology, 4(3):187-200. (in Farsi)
Jang, J. S. R., Sun. C. T., Mizutani, E. 1997. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, New Jersey.
Joorabian, M., Hooshmand, R, 2003, Fuzzy logic and neural networks (concepts and applications), Shahid Chamran University of Ahvaz, 300 pages. (In Farsi)
Joorabian, M., Zare, T., Ostvar, A. 2005. Artificial neural networks, Shahid Chamran University of Ahvaz, 716 pages. (In Farsi)
Mohammadi, K., Shamshirband, S., Tong, C. W., Alam, K. A., Petković, D. 2015. Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year. Energy Conversion and Management, 93: 406-413.
Moradi, I. 2009. Quality control of global solar radiation using sunshine duration hours. Energy, 34(1): 1-6.
Muneer, T., Gueymard, C., Kambezidis H. 2004. Solar radiation and day light models. Burlington. Elsevier. 392 pages.
Piri, J., Ansari, H; Faridhosseini, A. 2013. Modeling of solar radiation by using experimental models and ANFIS. (Case study: Zahedan and Bojnoord stations). Journal of Iran Energy, 16(3): 37-58. (in Farsi)
Prescott, J. A. 1940. Transactions of the Royal Society of South Australia, 46: 114-118.
Quej, V. H., Almorox, J., Arnaldo, J. A., Saito, L. 2017. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. Journal of Atmospheric and Solar-Terrestrial Physics, 155: 62-70.
Rivington, M., Matthews, K. B., Buchan, K. 2002. A Comparison of Methods for Providing Solar Radiation Data to Crop Models and Decision Support Systems.
Sabziparvar, A. A., Bayat Varkeshi, M. 2010. Evaluate the accuracy of artificial neural network and neuro fuzzy methods in simulated solar radiation. Iranian Journal of physics Research, 10(4): 347-357. (In Farsi)
Salisu, S. 2017. New model for solar radiation estimation from measured air temperature and relative and humidity in Nigeria. Journal Publishing Practices and Standards (JPPS), 36(3): 917-922.
Thornton, P. E., Running, S. W. 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93(4): 211-228.
Yorukoglu, M., Celik, A. N. 2006. A critical review on the estimation of daily global solar radiation from sunshine duration. Energy Conversion and Management, 47(15-16): 2441-2450.
Zou, L., Wang, L., Xia, L; Lin, A., Hu, B., Zhu, H. 2017. Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems. Renewable Energy, 106: 343-353.