simP: PFS similarity measure simP

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

PFS similarity measure simP

Description

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

Usage

simP(ma, na, mb, nb, a, b, p, t, 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
a	Level of uncertainty values, values range from 1 to 10
b	Level of uncertainty values, values range from 1 to 10
p	Lp norm values for measuring the p-norm distance between x and y, values range from 1 to 5
t	Level of uncertainty values, values range from 1 to 10
k	A constant value, considered as 1

Value

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

References

X. Peng. New similarity measure and distance measure for pythagorean fuzzy set. Complex & Intelligent Systems, 5(2):101 - 111, 2019.

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)
a<-2
b<-2
p<-2
t<-2
k<-1
simP(ma,na,mb,nb,a,b,p,t,k)
#[1] 0.7007663 0.6879639 0.8834981 0.8834981

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