Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

EM algorithm based on fuzzy observations

Document Type : Original Article

Author
Abbas Parchami
20.1001.1.27174409.1397.1.2.3.6/DOR
Abstract
The EM algorithm is a powerful tool for estimating maximum accuracy based on incomplete data and has been discussed in most statistical inference books. Here, the meaning of the word "incomplete" is general, and in different situations it can mean a variety of meanings (such as missing data, interval data, censored observations, and the like).
This paper introduces a new application of the EM algorithm in which incomplete data refers to inaccurate / ambiguous / fuzzy data. Based on this type of ambiguous data, in this paper, the maximum likelihood estimate of the exponential distribution parameter is calculated using the EM algorithm in the form of a numerical example, and this example can be used to understand the material and also use the EM algorithm in the examples / modes. More complex, useful for graduate students based on fuzzy observations
Keywords
EM algorithm estimates the maximum likelihood of fuzzy data
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Fuzzy Systems and its Applications
Volume 1, Issue 2 - Serial Number 2
Open Access Statement
December 2018
Pages 35-44

  • Receive Date 16 June 2018
  • Accept Date 16 April 2019