TY - JOUR
ID - 1606
TI - Improvement of Rule Generation Methods for Fuzzy Controller
JO - Journal of AI and Data Mining
JA - JADM
LA - en
SN - 2322-5211
AU - Mohammadkarimi, N.
AU - Derhami, V.
AD - Department of Computer Engineering, Yazd University, Yazd, Iran.
Y1 - 2020
PY - 2020
VL - 8
IS - 1
SP - 49
EP - 54
KW - Fuzzy controller
KW - Fuzzy rule generation
KW - Inconsistent training data
DO - 10.22044/jadm.2018.5593.1670
N2 - This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to improvement the above challenge. A zero order Sugeno fuzzy model used as fuzzy system structure. At first by using clustering obtains the number of rules and input membership functions. A set of candidate amounts for consequence parts of fuzzy rules is considered. Considering each pair of training data, according which rules fires and what is the output in the pair, the amount of probability of consequences candidates are change. In the next step, eligibility probability of each consequence candidate for all rules is determined. Finally, using these obtained probability, two probable outputs is generate for each input. The experimental results show superiority of the proposed approach rather than some available well-known approaches that makes reduce the number of rule and reduce system complexity.
UR - https://jad.shahroodut.ac.ir/article_1606.html
L1 - https://jad.shahroodut.ac.ir/article_1606_81de3d152fdb61a778f578a099734a6a.pdf
ER -