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.
Bibliographical noteFunding Information:
This work was supported by Manpower Development Program for Energy & Resources of MKE with Yonsei Electric Power Research Center (YEPRC) at Yonsei University, Seoul, Korea. The corresponding author also appreciates Prof. Lotfi A. Zadeh for the facilities provided in BISC (Berkeley Initiative in Soft Computing) when the manuscript is prepared.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Theoretical Computer Science
- Applied Mathematics