fuzzyscalar {fuzzyOP} | R Documentation |
Fuzzy numbers are multiplied with a scalar.
fuzzyscalar(anz, A, vektor, v, pl)
anz |
Number of the delta-cuts. |
A |
Dataset, which must have at least 2 columns. |
vektor |
Vector, that describes which fuzzy numbers from the dataset are selected. |
v |
A scalar. |
pl |
Plot-option, 0 or 1 or 2. |
For more information about the necessary data format see: test
The multiplication of the fuzzy numbers with a scalar is calculated and if the
plot-option is set to 1 the characterizing function of the multiplication with a scalar is depicted.
If the option 2 is selected, then
the characterizing functions of the fuzzy numbers are plotted in one plot and the
characterizing function of the multiplication with a scalar in another one.
If the plot-option is set to 0, then no plot is produced.
Output data is of the same type as input data.
Therefore the returned value is a matrix containing the multiplication of fuzzy numbers with a scalar, which
are again piecewise linear fuzzy numbers.
The odd columns contain the x-values and the even columns the values of the
characterizing function of the supporting points
of the multiplication of the fuzzy numbers with a scalar.
Semagül Aklan, Emine Altindas, Yi Hong Kang, Rabiye Macit, Senay Umar, Hatice Ünal
R. Viertl, D. Hareter: Beschreibung und Analyse unscharfer Information - Statistische Methoden für unscharfe Daten, Springer, Wien, 2006
fuzzynumber
, fuzzymin
, fuzzymax
, fuzzydeltacut
, fuzzysum
, fuzzymean
, fuzzyproduct
, fuzzypower
, fuzzyfunction
require(fuzzyOP) ##Example 1: ##create data: a<-c(-1,0,1,NA,NA,NA) b<-c(0,1,0,NA,NA,NA) d<-c(1,2,3,4,5,6) e<-c(0,0.3,1,0.4,0.2,0) f<-c(1:5,NA) g<-c(0,0.2,1,0.5,0,NA) B<-cbind(a,b,d,e,f,g) ##execute: fuzzyscalar(100,B,1,5,2) ##Example 2: data(test) A<-fuzzyscalar(5,test,c(1,4,8),4,1) A