A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic gene...A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic generalized ordered weighted averaging （LGOWA） operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average （WA） and in the ordered weighted averaging （OWA） function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean （LGM）, the linguistic OWA （LOWA） operator and the linguistic or- dered weighted quadratic averaging （LOWQA） operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.展开更多
基金supported by the Spanish Ministry of Education(JC2009-00189)the Spanish Ministry of Foreign Affairs(A/023879/09)+1 种基金the National Natural Science Foundation of China(71071002)Academic Innovation Team of Anhui University(KJTD001B,SKTD007B)
文摘A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic generalized ordered weighted averaging （LGOWA） operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average （WA） and in the ordered weighted averaging （OWA） function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean （LGM）, the linguistic OWA （LOWA） operator and the linguistic or- dered weighted quadratic averaging （LOWQA） operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.
