Zhang, C. & Guo, P. (2018). FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation. Agricultural Water Management, vol. 199, pp.105–119.
 Tanaka, H., Uejima, S. & Asia, K. (1982). Linear regression analysis with fuzzy model, IEEE Transactions on Systems. Man and Cybernetics, vol. 12, pp. 903-907.
, A., Kaur
, J. & Singh
P. (2011). A new method for solving fully fuzzy linear programming problems. Applied Mathematical Modelling, vol. 35, no. 2
, pp. 817-823.
 Lotfi, F. H. T., Allahviranloo, M., Jondabeh, A. & Alizadeh, L. (2009). Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution. Applied Mathematical Modelling, vol. 33, pp.3151–3156.
 Goudarzi, F. K., Nasseri, S. H.& Taghnezhad, N. A. (2020). A new interactive approach for solving fully fuzzy mixed integer linear programming problems. Yugoslav Journal of Operations Research, vol. 30, no. 1, pp.71-89.
 Diamond, P. (1988). Fuzzy least squares. Information Sciences, vol. 46, no. 3, pp.141-157.
 Tanaka, H. (1987). Fuzzy data analysis by possibilistic linear models. Fuzzy Sets and Systems, vol. 24, no. 3, pp.363-375.
 Tanaka, H., Hayashi, I. & Watada, J. (1989). Possibilistic
linear regression analysis for fuzzy data. European Journal of Operational Research, vol. 40, no. 3, pp. 389-396.
 Danesh, S., Farnoosh, R., Razzaghnia & T. (2016). Fuzzy nonparametric regression based on adaptive neuro-fuzzy inference system. Neurocomputing. vol. 173, pp. 1450-1460.
 Razzaghnia, T., Danesh, S. & Maleki, A. (2011). Hybrid fuzzy regression with trapezoidal fuzzy data. Proc. Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, Singapore, 2011.
 Razzaghnia, T. & Danesh, S. (2015). Nonparametric Regression with Trapezoidal Fuzzy Data. International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), vol. 3, no. 6, pp. 3826 – 3831.
 Danesh, S. (2018). Fuzzy Parameters Estimation via Hybrid Methods. Hacettepe Journal of Mathematics and Statistics, vol. 47, no. 6,
pp. 1605 – 16244.
 Razzaghnia T. & Pasha, E. (2009). A new mathematical programming approach in fuzzy linear regression models. Applied Mathematical Sciences, vol. 18, no. 70, pp50-59.
 AL-Othman, A.K. (2009). A fuzzy state estimator based on uncertain measurements. Measurement, vol. 42, no. 4, pp. 628-637.
, M., Deo
, R. C., Downs
, N. J. & Maraseni
T., An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index. Atmospheric Research, vol. 207
, no. 15, 2018, pp. 155-
 Danesh, M., Danesh, S. & Khalili K. (2019). Multi-Sensory Data Fusion System for Tool Condition Monitoring Using Optimized Artificial Fuzzy Inference System. Mechanics Aaerospace Journal, vol. 15, no. 2, pp. 103-118.
 Fogel, D.B. (1995). Evolutionary computation: toward a new philosophy of machine intelligence. New York, IEEE Press, pp. 87-121.
 DeJong, K. (1988). Learning with genetic algorithms: an overview. Mach Learn 3, pp. 121–138.
 Goldberg, DE. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, pp.1-15.
 Jang, J. S. R. (1992). Self-learning fuzzy controllers based on temporal back-propagation. IEEE Transactions on Neural Network, vol. 3, pp. 714-723.
 Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cyber, vvol. 23, no. 3, pp. 665-685.
 Takagi, T. & Sugeno, M. (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man and Cybernetics, vol. 15, pp. 116-132.
 Benardos, P. G., Mosialos, S. & Vosniakos, G.C. (2006). Prediction of workpiece elastic deflections under cutting forces in turning. Robotics and Computer-Integrated Manufacturing, vol. 22, pp. 505–514.
 Danesh, M. & Khalili, K. (2015). Determination of Tool Wear in Turning Process Using Undecimated Wavelet Transform and Textural Features. Procedia Technology, vol.19, pp. 98-105.
 Khalili, K. & Danesh, M. (2015). Identification of vibration level in metal cutting using undecimated wavelet transform and gray-level co-occurrence matrix texture features, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 229, pp. 205-213.
 Qiang, L.Z. (2000). Finite difference calculations of the deformations of multi-diameter workpieces during turning. Journal of Materials Processing Technology, vol. 98, pp. 310–316.
 Phan, A.V., Baron, L., Mayer, J.R.R. & Cloutier, G. (2003). Finite element and experimental studies of diametral errors in cantilever bar turning. Applied Mathematical Modelling, vol. 27, pp. 221–232.