BUILDING OF COMPACT AND ACCURACY FUZZY MODELS BASED ON STATISTICAL INFORMATION CRITERIA
This paper focuses on fuzzy models design using the statistical information criteria: 1) the Akaike information criterion; 2) the Bayes information criterion; and 3) the Hannan-Quinn information criterion. The training procedure is based on optimization and differential evolution. The performed analysis is based on ideal and real dataset showing the effectiveness of the proposed method.