simSWLX {ipsfs}

IFS similarity measure simSWLX

Description

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

Usage

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

Arguments

ma

IFS membership values for the data set x computed using either triangular or trapezoidal or guassian membership function

na

IFS non-membership values for the data set x computed using either Sugeno and Terano's or Yager's non-membership function

mb

IFS membership values for the data set y computed using either triangular or trapezoidal or guassian membership function

nb

IFS non-membership values for the data set y computed using either Sugeno and Terano's or Yager's non-membership function

ha

IFS hesitancy values for the data set x

hb

IFS hesitancy values for the data set y

k

A constant value, considered as 1

Value

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

References

Y. Song, X. Wang, L. Lei, and A. Xue. A new similarity measure between intuitionistic fuzzy sets and its application to pattern recognition. In Abstract and Applied Analysis, volume 2014. Hindawi, 2014.

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<-hmemIFS(ma,na)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
hb<-hmemIFS(mb,nb)
k<-1
simSWLX(ma,na,mb,nb,ha,hb,k)
#[1] 0.9241207 0.9180258 0.9853267 0.9853267