One important issue in the theory of Ordered Weighted Averaging (OWA) operators is the determination of the associated weights. One of the first approaches, suggested by O’Hagan, determines a special class of OWA operators having maximal entropy of the OWA weights for a given level of orness; algorithmically it is based on the solution of a constrained optimization problem. In this paper, using...

OWA operators, introduced by Yager, are very important non linear aggregation functions in both academic studies and a myriad of applications. In this study, we use two dimensional OWA aggregation function into pedagogical evaluation practice, which will involve the preferences and experiences of decision makers and teachers. In addition, we also introduce a long time educational evaluation mod...

The OWA operator proposed by Yager has been widely used to aggregate experts’ opinions or preferences in human decision making. Yager’s traditional OWA operator focuses exclusively on the aggregation of crisp numbers. However, experts usually tend to express their opinions or preferences in a very natural way via linguistic terms. These linguistic terms can be modelled or expressed by (type-1) ...

* On leave of absence from the Department of Electronics and Computers, Transylvania University of Braşov. Abstract – Ordered Weighted Aggregation (OWA) operators represent a distinct family of aggregation operators and were introduced by Yager in [1]. They compute a weighted sum of a number of criteria that must be satisfied. The central element of the OWA operators is that the criteria are re...

fuzzylogic computes a multi-criteria evaluation by means of either a boolean analysis, weightedlinear combination (wlc) and ordered weighted averaging (owa) of factor images. owa works withstandardized factor images and employs a variant of the wlc. it takes into account the risk associated withthe decision and degree of tradeoff associated with the variables in the analysis. in this research, ...

this paper will introduce a new method to obtain the order weightsof the ordered weighted averaging (owa) operator. we will first show therelation between fuzzy quantifiers and neat owa operators and then offer anew combination of them. fuzzy quantifiers are applied for soft computingin modeling the optimism degree of the decision maker. in using neat operators,the ordering of the inputs is not...

Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the over...

We consider different types of aggregation operators such as the heavy ordered weighted averaging (HOWA) operator and the fuzzy ordered weighted averaging (FOWA) operator. We introduce a new extension of the OWA operator called the fuzzy heavy ordered weighted averaging (FHOWA) operator. The main characteristic of this aggregation operator is that it deals with uncertain information represented...

The determination of ordered weighted averaging (OWA) operator weights is a very important issue of applying the OWA operator for decision making. One of the first approaches, suggested by O’Hagan, determines a special class of OWA operators having maximal entropy of the OWA weights for a given level of orness; algorithmically it is based on the solution of a constrained optimization problem. I...

We study different types of aggregation operators. We focus on the generalized OWA (GOWA) operator developed by Yager which represents a generalization to a wide range of aggregation operators. We distinguish between aggregations with a descending or with an ascending order. We introduce the induced generalized OWA (IGOWA) operator which represents an extension to the GOWA operator. It generali...