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A unified approach to aggregation based upon mom and mam operators
Author(s) -
Yager Ronald R.
Publication year - 1995
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550100903
Subject(s) - generalization , associative property , fuzzy logic , artificial neural network , mathematics , property (philosophy) , operator theory , class (philosophy) , focus (optics) , computer science , algebra over a field , discrete mathematics , artificial intelligence , pure mathematics , mathematical analysis , philosophy , physics , optics , epistemology
The focus of this article is on the issue of information aggregation. We introduce two new aggregation operators, called MOM and MAM operators, which are, respectively, generalized and and or operators. We describe their relationship to the multivalued logic triangular norm operators and show how they generalize these operators by weakening the associativity property. We provide a duality theorem between these new operators. We present some special classes of these operators. We extend these operators to allow for weighted aggregations, which enable us to include importances. We introduce some families of these weighted MOM and MAM operators. We show how the typical neural aggregation is a special class of these weighted MOM and MAM operators. This generalization allows us to consider neural network and fuzzy logic methods in the same framework. © 1995 John Wiley & Sons, Inc.