A new probabilistic fuzzy model: Fuzzification-Maximization (FM) approach

Sungjun Hong, Heesung Lee, Euntai Kim

Research output: Contribution to journalArticle

3 Citations (Scopus)


Over the past few decades, fuzzy logic systems have been used for nonlinear modeling and approximation in many fields ranging from engineering to science. In this paper, a new fuzzy model is developed from the probabilistic and statistical point of view. The proposed model decomposes the input-output characteristics into noise-free part and probabilistic noise part and identifies them simultaneously. The noise-free model recovers the nominal input-output characteristics of the target system and the noise model gives approximation to the probabilistic nature of the added noise. To identify the two submodels simultaneously, we propose the Fuzzification-Maximization (FM). Finally, some simulations are conducted and the effectiveness of the proposed method is demonstrated through the comparison with the previous methods.

Original languageEnglish
Pages (from-to)1129-1147
Number of pages19
JournalInternational Journal of Approximate Reasoning
Issue number7
Publication statusPublished - 2009 Jul 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'A new probabilistic fuzzy model: Fuzzification-Maximization (FM) approach'. Together they form a unique fingerprint.

  • Cite this