| simHY2 {ipsfs} | R Documentation |
IFS similarity measure simHY2
Description
IFS similarity measure values using simHY2 computation technique with membership, and non-membership values of two objects or set of objects.
Usage
simHY2(ma, na, mb, nb, 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 |
k |
A constant value, considered as 1 |
Value
The IFS similarity values of data set y with data set x
References
W.-L. Hung and M.-S. Yang. On similarity measures between intuitionistic fuzzy sets. International journal of intelligent systems, 23(3):364 - 383, 2008.
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)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
k<-1
simHY2(ma,na,mb,nb,k)
#[1] 0.7247430 0.7253651 0.9021400 0.9021400