fuzzymean {fuzzyOP} | R Documentation |
The arithmetic mean of fuzzy numbers is computed.
fuzzymean(anz, A, vektor, 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. |
pl |
Plot-option, 0 or 1 or 2. |
For more information about the necessary data format see: test
The arithmetic mean of the fuzzy numbers is calculated, and if the plot-option
is set to 1 the characterizing function of the mean 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 mean 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 arithmetic mean of fuzzy numbers, which is
again a piecewise linear fuzzy number.
The first column describes the x-values and the second one the values of the
characterizing function of the supporting points of the arithmetic mean
of the fuzzy numbers.
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
, fuzzyscalar
, 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: fuzzymean(100,B,c(1,2),2) ##Example 2: data(test) A<-fuzzymean(5,test,c(1,4,8),1) A