weightedf: Weighted extension of the OWA function

wowa.weightedfR Documentation

Weighted extension of the OWA function

Description

Function for extending order weigted averages and other multivariate symmetric functions

Usage

  wowa.weightedf(x, p, w, n, Fn, L)

Arguments

x

The vector of inputs

p

The weights of inputs x

w

The OWA weightings vector

n

The dimension of the vector x

Fn

Base n-variate symmetric function defined in R

L

The number of levels of the n-ary tree (see docs)

Value

output

The output is the weighted ordered weigted average.

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

  
      Fn <- function(n, x, w) {
  	  out <- 0.0
	  for(i in 1:n) out<- out+x[i]*w[i];
	  #print(out)
          return(out)
       }
      n <- 4

        example <- wowa.weightedf(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, Fn,  10)
	example
    
  

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

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