wowa.weightedOWAQuantifierBuild {wowa}

RIM quantifier of the Weighted OWA function

Description

Function for building the RIM quantifier of the Weighted OWA function

Usage

  wowa.weightedOWAQuantifierBuild(p, w, n)

Arguments

p

The weights of inputs x

w

The OWA weightings vector

n

The dimension of the vectors p,w

Value

output

A structure which has fields: spl, which keeps the spline knots and coefficients for later use in weightedOWAQuantifier, and Tnum, the number of knots in the monotone spline

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
     pweights=c(0.3,0.25,0.3,0.15);
     wweights=c(0.4,0.35,0.2,0.05);
     tspline <- wowa.weightedOWAQuantifierBuild(pweights,wweights , n)
     wowa.weightedOWAQuantifier(c(0.3,0.4,0.8,0.2), pweights, wweights, n, tspline)