Abstract
Hong (Mathematics 2019, 7, 326) recently introduced the general least squares deviation (LSD) model for ordered weighted averaging (OWA) operator weights. In this paper, we propose the corresponding generalized least square disparity model for regular increasing monotone (RIM) quantifier determination under a given orness level. We prove this problem mathematically. Using this result, we provide the full solution of the least square disparity RIM quantifier model as an illustrative example.
Highlights
One of the important topics in the theory of ordered weighted averaging (OWA) operators is the determination of the associated weights
Information aggregation procedures guided by verbally expressed concepts and a dimension-independent description of the desired aggregation can be provided by regular increasing monotone (RIM) quantifiers
We provide the least square disparity (LSD) RIM quantifier model as an illustrative example
Summary
One of the important topics in the theory of ordered weighted averaging (OWA) operators is the determination of the associated weights. Hong [21] provided generalized solutions to the maximum entropy RIM quantifier problem and minimax ratio RIM quantifier problem. Suggested a general RIM quantifier determination model, and proved it analytically using the optimal control method, and proved the solution equivalence to the minimax problem for the RIM quantifier. Hong and Han [10] recently provided the following general model for the least squares deviation (LSD) method as an alternative approach to determine the OWA operator weights:. We provide the least square disparity (LSD) RIM quantifier model as an illustrative example
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.
