simWW2: PFS similarity measure simWW2

In ipsfs: Intuitionistic, Pythagorean, and Spherical Fuzzy Similarity Measure

PFS similarity measure simWW2

Description

PFS similarity measure values using simWW2 computation technique with membership,non-membership, and hesitancy values of two objects or set of objects.

Usage

simWW2(ma, na, mb, nb, ha, hb, k)

Arguments

ma	PFS membership values for the data set x computed using either triangular or trapezoidal or guassian membership function
na	PFS non-membership values for the data set x computed using either Sugeno and Terano's or Yager's non-membership function
mb	PFS membership values for the data set y computed using either triangular or trapezoidal or guassian membership function
nb	PFS non-membership values for the data set y computed using either Sugeno and Terano's or Yager's non-membership function
ha	PFS hesitancy values for the data set x
hb	PFS hesitancy values for the data set y
k	A constant value, considered as 1

Value

The PFS similarity values of data set y with data set x

References

G.Wei and Y.Wei. Similarity measures of pythagorean fuzzy sets based on the cosine function and their applications. International Journal of Intelligent Systems, 33(3):634 - 652, 2018.

Examples

x<-matrix(c(12,9,14,11,21,16,15,24,20,17,14,11),nrow=4)
y<-matrix(c(11,21,6),nrow=1)
a<-mn(x)
b<-std(x)
a1<-mn(y)
b1<-std(y)
lam<-0.5
ma<-memG(a,b,x)
na<-nonmemS(ma,lam)
ha<-hmemPFS(ma,na)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
hb<-hmemPFS(mb,nb)
k<-1
simWW2(ma,na,mb,nb,ha,hb,k)
#[1] 0.7061971 0.6841839 0.9511029 0.9511029

ipsfs documentation built on June 21, 2022, 5:07 p.m.