TY - JFULL AU - Shih-Pin Chen and Shih-Syuan You PY - 2014/10/ TI - A Fuzzy Linear Regression Model Based on Dissemblance Index T2 - International Journal of Mathematical and Computational Sciences SP - 1277 EP - 1283 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/10003945 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 93, 2014 N2 - Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy. ER -