TY - JOUR ID - 12304 TI - fuzzy parameter estimation via fuzzy weights and linear programming JO - Journal of Advanced Mathematical Modeling JA - JAMM LA - en SN - 2251-8088 AU - Danesh, Sedigheh AU - Frnoosh, Rahman AU - Razzaghnia, Tahereh AD - Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran AD - School of Mathematics, Iran University of Science and Technology, Tehran, Iran AD - Department of Statistics, Roudehen Branch, Islamic Azad University, Roudehen, Iran Y1 - 2016 PY - 2016 VL - 6 IS - 1 SP - 61 EP - 80 KW - Fuzzy Regression KW - Linear programming KW - quadratic programming KW - adaptive neuro fuzzy inference system DO - 10.22055/jamm.2016.12304 N2 - Fuzzy regression represents the relation between variables. Fuzzy regression analysis is one of the most widely used statistical techniques. In this study, fuzzy regression is considered with the crisp input and the fuzzy output. A hybrid algorithm based on fuzzy weights and linear programming is designed for the fuzzy nonparametric regression model prediction that function form is assumed unknown and in cases low data. In proposed method, the objective function minimizes the spread of outputs. Finally, the performance of the suggested method is compared with linear programming (LP) and quadratic programming (QP) methods using the numerical examples. The results demonstrate that the proposed method has more accurate than the LP and QP methods. Also, it is verified in cases of low data. UR - https://jamm.scu.ac.ir/article_12304.html L1 - https://jamm.scu.ac.ir/article_12304_0cd56c5bc5d9fba36cc54ab20359ce65.pdf ER -