We approach the problem of measuring consensus for a set of real inputs by aggregating the fuzzy implication degrees between each pair of inputs.

Abstract—We approach the problem of measuring consensus for a set of real inputs by aggregating the fuzzy implication degrees between each pair of inputs.

This work approaches the problem of measuring consensus for a set of real inputs by aggregating the fuzzy implication degrees between each pair of inputs by ...

The purpose of this paper is to develop a method for aggregating experts' fuzzy estimates into a group consensus under group decision environment. Fuzzy numbers ...

We approach the problem of measuring consensus for a set of real inputs by aggregating the fuzzy implication degrees between each pair of inputs.

Jun 4, 2017 · Gleb Beliakov , Simon James , Tomasa Calvo : Aggregating fuzzy implications to measure group consensus. IFSA/NAFIPS 2013: 1016-1021.

In the proposed construction, a consensus measure depends on a fuzzy entropy that may be viewed as the generator of the proposed consensus measure. This ...

We focus on the problem of constructing functions that are able to measure the degree of consensus for a set of inputs provided over the unit interval.

Oct 22, 2024 · ... A fuzzy entropy-based approach to the measurement of consensus for the case where the inputs are dichotomous variables will be presented in ...

We focus on the problem of constructing functions that are able to measure the degree of consensus for a set of inputs provided over the unit interval.