wowa (version 1.0.2)

wowa.weightedOWAQuantifierBuild: 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

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

# NOT RUN {
     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)
# }