By Topic

Fuzzy Regression Models Using the Least-Squares Method Based on the Concept of Distance

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Liang-Hsuan Chen ; Dept. of Ind. & Inf. Manage., Nat. Cheng Kung Univ., Tainan, Taiwan ; Chan-Ching Hsueh

Fuzzy regression models are developed to construct the relationship between explanatory variables and responses in a fuzzy environment. In order to increase the explanatory performance of the model, the least-squares method is applied to determine the numeric coefficients based on the concept of distance. Unlike most existing approaches, the numeric coefficients in the proposed model can have negative values. The proposed model minimizes total estimation error in terms of the sum of the average squared distance between the observed and estimated responses based on a few alpha-cuts. The proposed approach is not limited to triangular fuzzy numbers; it can be used to carry out a large number of fuzzy observations efficiently because the model is based on traditional statistical methods. Comparisons with existing methods show that based on the total estimation error using the mean squared error and Kim and Bishu's criterion, the explanatory performance of the proposed model is satisfactory.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:17 ,  Issue: 6 )