[1] Lodwick, W.A. (2021). Fuzzy, Possibility, Probability, and Generalized Uncertainty Theory in Mathematical Analysis. Journal of Mahani Mathematical Research, Vol.10, Issue 2, pp.73-101.
[2] Zadeh, L.A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, Vol. 100, Supplement 1, pp. 9-34.
[3] Dubois D., Prade H. (1998). Possibility Theory: Qualitative and Quantitative Aspects. In: Smets P. (eds) Quantified Representation of Uncertainty and Imprecision. Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 1, Springer, Dordrecht.
[4] Pyt’ev Y.P. and Zhivotnikov G. S. (2004). On the methods of possibility theory for morphological image analysis. Pattern Recognition, Image Analysis, 14 (1), pp.60–71.
[5] Dubois, D., Prade, H. (2012). Possibility theory: an approach to computerized processing of uncertainty. Springer Science Business Media.
[6] Knill, D. C., Richards, W. (Eds.). (1996). Perception as Bayesian inference. Cambridge University Press.
[7] Stroock, D. W. (2000). Probability Theory: An Analytic View. Cambridge: Cambridge University Press.
[8] Jaynes E.T. and Bretthorst G. L. (2003). Probability Theory: The Logic of Science. Cambridge University Press.
[9] Devroye L., Györfi L., Lugosi G. (2013). A Probabilistic Theory of Pattern Recognition. Springer; Softcover reprint of the original 1st ed. 1996 edition (November 22, 2013).
[10] Medasani, S., Kim, J., Krishnapuram, R. (1998). An overview of membership function generation techniques for pattern recognition. International Journal of approximate reasoning, 19(3-4), 391-417.
[11] Bezdek, J. C., Keller, J., Krisnapuram, R., Pal, N. (1999). Fuzzy models and algorithms for pattern recognition and image processing (Vol. 4). Springer Science Business Media.
[12] Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science Business Media.
[13] Fukunaga, K., Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on information theory, 21(1), 32-40.
[14] Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.
[15] Jain, A. K. (1987). Advances in statistical pattern recognition. In Pattern recognition theory and applications (pp. 1-19). Springer, Berlin, Heidelberg.
[16] Cha, S. H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. City, 1(2), 1.
[17] Kohonen, T. (1990). Statistical pattern recognition revisited. In Advanced neural computers (pp. 137-144). North-Holland.
[18] Pau, L. F. (1982). Fusion of multisensor data in pattern recognition. In Pattern recognition theory and applications (pp. 189-201). Springer, Dordrecht.
[19] Repp, B. H., Milburn, C., Ashkenas, J. (1983). Duplex perception: Confirmation of fusion. Perception Psychophysics, 33(4), 333-337.
[20] Della Riccia, G., Lenz, H. J., Kruse, R. (Eds.). (2001). Data fusion and perception. Springer.
[21] Sun, Q. S., Zeng, S. G., Liu, Y., Heng, P. A., Xia, D. S. (2005). A new method of feature fusion and its application in image recognition. Pattern Recognition, 38(12), 2437-2448.
[22] Schwenker, F., Dietrich, C., Thiel, C., Palm, G. (2006). Learning of decision fusion mappings for pattern recognition. International Journal on Artificial Intelligence and Machine Learning (AIML), 6, 17-21.
[23] Hak, T., Dul, J. (2009). Pattern matching.
[24] Hoffmann, C. M., O’Donnell, M. J. (1982). Pattern matching in trees. Journal of the ACM (JACM), 29(1), 68-95.
[25] Dubois, D., Prade, H., Testemale, C. (1988). Weighted fuzzy pattern matching. Fuzzy sets and systems, 28(3), 313-331.
[26] Cayrol, M., Farreny, H., Prade, H. (1982). Fuzzy pattern matching. Kybernetes.
)