In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y = f(X)+epsilon.. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the 'switching regression model' and it is expressed with Y-L = f(L)(X) + epsilon(L) (L = Pi(p)(i=1) l(i)). Here l(i) indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R.Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, journal of the American Statistical Association 67 (338) (1972) 306-310].