ImplicitWOWA: Impicit Weighted OWA Computation Function

In wowa: Weighted Ordered Weighted Average

Impicit Weighted OWA Computation Function

Description

Function for Calculating implicit Weighted OWA function

Usage

  wowa.ImplicitWOWA(x, p, w, n)

Arguments

x	The vector of inputs
p	The weights of inputs x
w	The OWA weightings vector
n	Dimension of the vector x

Value

output

The value of the Impicit Weighted OWA

Author(s)

Gleb Beliakov, Daniela L. Calderon, Deakin University

References

[1]G. Beliakov, H. Bustince, and T. Calvo. A Practical Guide to Averaging Functions. Springer, Berlin, Heidelberg, 2016.

[2]G. Beliakov. A method of introducing weights into OWA operators and other symmetric functions. In V. Kreinovich, editor, Uncertainty Modeling. Dedicated to B. Kovalerchuk, pages 37-52. Springer, Cham, 2017.

[3]G. Beliakov. Comparing apples and oranges: The weighted OWA function, Int.J. Intelligent Systems, 33, 1089-1108, 2018.

[4]V. Torra. The weighted OWA operator. Int. J. Intelligent Systems, 12:153-166, 1997.

[5]G. Beliakov and J.J. Dujmovic , Extension of bivariate means to weighted means of several arguments by using binary trees, Information sciences, 331, 137-147, 2016.

[6] J.J. Dujmovic and G. Beliakov. Idempotent weighted aggregation based on binary aggregation trees. Int. J. Intelligent Systems 32, 31-50, 2017.

Examples

    n <- 4
    example <- wowa.ImplicitWOWA(c(0.3,0.4,0.8,0.2), c(0.3,0.25,0.3,0.15), 
                     c(0.4,0.35,0.2,0.05), n)
    example	
  

wowa documentation built on May 24, 2022, 5:05 p.m.