simPG2: PFS similarity measure simPG2

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

PFS similarity measure simPG2

Description

PFS similarity measure values using simPG2 computation technique with membership, and non-membership values of two objects or set of objects.

Usage

simPG2(ma, na, mb, nb, p, l, 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
p	Lp norm values for measuring the p-norm distance between x and y, values range from 1 to 5
l	Level of uncertainty values, values range from 1 to 10
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 and H. Garg. Multiparametric similarity measures on pythagorean fuzzy sets with applications to pattern recognition. Applied Intelligence, 49(12):4058 - 4096, 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)
p<-2
l<-2
t<-2
k<-1
simPG2(ma,na,mb,nb,p,l,t,k)
#[1] 0.5203669 0.5000073 0.7998594 0.7998594

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