simZ {ipsfs}

PFS similarity measure simZ

Description

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

Usage

simZ(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

X. Zhang. A novel approach based on similarity measure for pythagorean fuzzy multiple criteria group decision making. International Journal of Intelligent Systems, 31(6):593 - 611, 2016.

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
simZ(ma,na,mb,nb,ha,hb,k)
#[1] 0.6128632 0.6335697 0.7722389 0.7722389