Pano-Azucena, Ana Dalia, Esteban Tlelo-Cuautle, and Sheldon X-D. Tan. ”Prediction of chaotic time series by using ANNs, ANFIS and SVMs.” Modern Circuits and Systems Technologies (MOCAST), 2018 7th International Conference on, IEEE, (2018).
 M. Brown and C. J. Harris, Prentice Hall, Hemel Hempstead, Neurofuzzy Adaptive Modelling and Control, (1994).
 J. Castro and M. Delgado, ”Fuzzy Systems with Defuzzification are Universal Approximators”, IEEE Trans. on Systems, Man and Cybernetics, Vol. 26, (1996), pp. 149-52.
 Chen, De-Wang, and Jun-Ping Zhang. ”Time series prediction based on ensemble AN FIS.” Machine Learning and Cybernetics, Proceedings of 2005 International Conference on. Vol. 6. IEEE, (2005).
 S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, (2008), Pages 750-757, ISSN 0925- 4005.
 Jang, J-SR, and C-T. Sun. ”Functional equivalence between radial basis function networks and fuzzy inference systems.” IEEE transactions on Neural Networks, Vol. 4.1, (1993), 156-159.
 E. Lotfi and M. Akbarzadeh-T., ”Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices”, Neurocomputing, Vol. 126, (2014), pp. 188- 196.
 E. H. Mamdani and S. Assillan, ”An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller”, Int. Journal of Man-Machine Studies, Vol. 7, (1975), pp. 1-13.
 Sarıca, Busenur, Erol Eğrioğlu, and Barış Aşıkgil. ”A new hybrid method for time series forecasting: AR–ANFIS.” Neural Computing and Applications, Vol. 29.3, (2018), 749- 760.
 Soman, Saurabh S., et al. ”A review of wind power and wind speed forecasting methods with different time horizons.” North American power symposium (NAPS), (2010. IEEE).
 L. Wang and J. M. Mendel, ”Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least Squares”, IEEE Trans. on Neural Networks, Vol. 3, (1992), pp. 807-814.