Abstract
There are several procedures such as possibilistic and least-square methods to estimate regression models. In this study, first, a fully fuzzy regression equation is converted into a fully fuzzy linear framework. By considering a least-square approach, a model is suggested based on matrix equations for solving fully fuzzy regression models. The main advantage of this method over existing ones is that this method considered values based on their specification, and all linear problems can be easily solved. Moreover, a case study for solid mechanics about the quantity of beam momentum is considered. In this example, the inner data are force values, and the output is momentum values.
Highlights
Regression assessment is concerned with statistical methodologies’ collection for simulation, solving, and researching the correlation between response variables and regressor or predictor ones. e examples of regression analysis applications are various and occur in almost all applied fields consisting of engineering, chemical, physics, biology, social science, management, and economics
Some observations are considered in the fuzzy form [1,2,3,4]. erefore, how to estimate regression coefficients and make the subsequent prediction under a classic environment is a dominant challenge to the classical regression analysis
A few studies have focused on the situations that consider both input and output variables in the fuzzy form
Summary
Regression assessment is concerned with statistical methodologies’ collection for simulation, solving, and researching the correlation between response variables and regressor or predictor ones. e examples of regression analysis applications are various and occur in almost all applied fields consisting of engineering, chemical, physics, biology, social science, management, and economics. The main nature of regression models is the estimation of the parameters employing possibility and least square approaches. D’Urso [32] proposed an iterative least square approach to study all possible combinations of fuzzy/crisp input-fuzzy/crisp output in detail. E estimation of regression parameters in linear models leads to a series of equations in that solving is somewhat difficult in fuzzy forms and especially fully fuzzy states. In another study [39], these researchers proposed a new fuzzy additive regression model with nonfuzzy predictors and fuzzy responses. Fully fuzzy linear regression matrix equations obtained by the least-square approach through the latter one are solved.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
