Shahrood University of TechnologyJournal of AI and Data Mining2322-52118120200101Improvement of Rule Generation Methods for Fuzzy Controller4954160610.22044/jadm.2018.5593.1670ENN. MohammadkarimiDepartment of Computer Engineering, Yazd University, Yazd, Iran.V. DerhamiDepartment of Computer Engineering, Yazd University, Yazd, Iran.0000-0003-4691-0643Journal Article20170423This 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.https://jad.shahroodut.ac.ir/article_1606_81de3d152fdb61a778f578a099734a6a.pdf