Parameters Estimation of Multidimensional Possibility Distributions
Authors: Sergey Sorokin, Irina Sorokina, Alexander Yazenin
Abstract:
We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation.
Keywords: Possibility distribution, parameters estimation, Maxmin u/E estimator, fuzzy model identification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1098994
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[1] Cai Kai-Yuan: Parameter estimations of normal fuzzy variables, Fuzzy Sets and Systems 55 (1993) 179-185.
[2] Dug Hun Hong: Parameter estimations of mutually T-related fuzzy variables, Fuzzy Sets and Systems 123 (2001) 6371.
[3] Wang Xizhao, Ha Minghu: Note on maxmin μ/E estimation. Fuzzy Sets and Systems 94 (1998) 71-75.
[4] W. N¨ather, On possibilistic inference, Fuzzy Sets and Systems 36 (1990) 327-337.
[5] Wang Xizhao, Ha Minghu: Fuzzy linear regression analysis, Fuzzy Sets and Systems 52 (1992) 179 -188.
[6] W. Zhenyuan, L. Shoumei: Fuzzy linear regression analysis of fuzzy-valued variables, Fuzzy Sets and Systems 36 (1990) 125 - 136.
[7] Steven Nahmias: Fuzzy variables. Fuzzy Sets and Systems 1 (1978) 97-110.
[8] A.V. Yazenin: On the problem of possibilistic optimization. Fuzzy Sets and Systems 81 (1996) 133-140.
[9] M.B. Rao, A. Rashed: Some comments on fuzzy variables. Fuzzy Sets and Systems 6 (1981) 285-292.
[10] Hans-Peter Schr¨ocker: Uniqueness Results for Minimal Enclosing Ellipsoids, Computer Aided Geometric Design 25, Issue 9, (December 2008) 756-762.
[11] Stephen L. Chiu: Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, Vol. 2, (1994), 267-278.
[12] A.S. Lapedes, R. Farber: Nonlinear signal processing using neural networks: predictions and system modelling. Tech. Rep. LA-UR-87-2662, Los Alamos Nat. Lab., Los Alamos, NM, (1987).
[13] JSR Jang: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. on Systems, Man & Cybernetics 23(3), (1993), 665-685.
[14] R.S. Crowder: Predicting the Mackey-Glass time series with cascade-correlation learning. In Proc. 1990 Connectionist Models Summer School, Carnegie Mellon University, (1990), 117-123.