Algorithm for Setting Fuzzy Logical Inclusion Systems Based on Statistical Data
Mikhail Golosovskiy, А. В. Богомолов, Dmitriy Tobin
Abstract
An original algorithm for tuning zero-order Sugeno-type fuzzy inference systems based on statistical data is presented. The algorithm is based on selecting areas around the reference points, finding the coordinates of the center of mass of the selected areas, and using them to set up a fuzzy inference system. A convergence theorem is proven for the proposed algorithm. The paper presents the results of studying the quality of the algorithm under conditions of changing the number of membership functions of input variables and the number of statistical data points, on the basis of which the fuzzy inference systems were tuned.
Topics & Concepts
Computer scienceFuzzy logicData miningInclusion (mineral)MathematicsLogical data modelArtificial intelligenceMachine learningPsychologyData modelingSocial psychologyDatabaseFuzzy Logic and Control SystemsNeural Networks and ApplicationsAdvanced Data Processing Techniques