Fitting generated aggregation operators to empirical data

被引：22

作者：

Beliakov, G

Mesiar, R

Valaskova, L

机构：

[1] Deakin Univ, Sch Informat Technol, Burwood 3125, Australia

[2] Slovak Univ Technol Bratislava, Fac Civil Engn, Dept Math, SK-81368 Bratislava, Slovakia

来源：

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS | 2004年 / 12卷 / 02期

关键词：

aggregation operators; generated operators; triangular norms; uninorms; Choquet integral;

D O I：

10.1142/S0218488504002783

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper treats the problem of fitting general aggregation operators with unfixed number of arguments to empirical data. We discuss methods applicable to associative operators (t-norms, t-conorms, uninorms and nullnorms), means and Choquet integral based operators with respect to a universal fuzzy measure. Special attention is paid to k-order additive symmetric fuzzy measures.

引用

页码：219 / 236

页数：18

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