Fuzzy linear regression analysis for fuzzy input-output data
Information Sciences: an International Journal
Properties of certain fuzzy linear regression methods
Fuzzy Sets and Systems
General fuzzy piecewise regression analysis with automatic change-point detection
Fuzzy Sets and Systems
Fuzzy regression wiht radial basis function network
Fuzzy Sets and Systems
Fuzzy least-squares linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems - Information processing
A note on fuzzy regression model with fuzzy input and output data for manpower forecasting
Fuzzy Sets and Systems - Theme: Learning and modeling
Multiple regression with fuzzy data
Fuzzy Sets and Systems
Interval regression analysis by quadratic programming approach
IEEE Transactions on Fuzzy Systems
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Fuzzy linear regression based on Polynomial Neural Networks
Expert Systems with Applications: An International Journal
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To handle the large variation issues in fuzzy input-output data, the proposed quadratic programming (QP) method uses a piecewise approach to simultaneously generate the possibility and necessity models, as well as the change-points. According to Tanaka and Lee [H. Tanaka, H. Lee, Interval regression analysis by quadratic programming approach, IEEE Transactions on Fuzzy Systems 6 (1998) 473-481], the QP approach gives more diversely spread coefficients than linear programming (LP) does. However, their approach only deals with crisp input and fuzzy output data. Moreover, their method is weak in handling fluctuating data. So far, no method has been developed to cope with the large variation problems in fuzzy input-output data. Hence, we propose a piecewise regression for fuzzy input-output data with a QP approach. There are three advantages in our method. First, the QP technique gives a more diversely spread coefficient than does a linear programming technique. Second, the piecewise approach is used to detect the change-points in the estimated model automatically, and handle the large variation data such as outliers well. Third, the possibility and necessity models with better fitness in data processing are obtained at the same time. Two examples are presented to demonstrate the merits of the proposed method.