New perspectives and applications of real-time fuzzy regression

Abstract

Fuzzy regression is one of important methods for data analysis. Fuzzy regression extends the concept of classical regression which has been constructed in the statistical framework. We show that a convex hull method can provide a powerful tool to reduce the computing time, especially for real-time data analysis. The main objective of this study is to propose an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. The reconstruction of convex hull edges depends on incoming vertices while a recomputing procedure can be implemented in real-time. An air pollution data is analyzed by applying the proposed approach. An important role of convex hull is emphasized in particular when dealing with the limitations of linear programming.