en:software:fem

# Fuzzy EM (E2M) algorithm

A method for estimating the parameters in a parametric statistical model when the observations are fuzzy and are assumed to be related to underlying crisp realizations of a random sample. This method is based on maximizing the observed-data likelihood defined as the probability of the fuzzy data.

The toolbox provides functions for the following problems:

• normal mean and variance estimation from trapezoidal fuzzy data;
• multiple linear regression with crisp inputs and trapezoidal fuzzy outputs;
• univariate finite normal mixture estimation from trapezoidal fuzzy data.

Reference:

T. Denoeux. Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets and Systems, accepted for publication, 2011, doi:10.1016/j.fss.2011.05.022. pdf