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

  • Authors:

  • Sungjun Hong;Heesung Lee;Euntai Kim

  • Affiliations:

  • School of Electrical and Electronic Engineering, Yonsei University, C613, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, C613, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, C613, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2009

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Abstract

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.