# Sd: Standard of Deviation of n Piecewise Linear Fuzzy Numbers In Sim.PLFN: Simulation of Piecewise Linear Fuzzy Numbers

## Description

This function is able to calculate the Standard of Deviation for a sample with size `n` from Piecewise Linear Fuzzy Numbers (PLFNs).

## Usage

 `1` ```Sd(S.PLFN) ```

## Arguments

 `S.PLFN` A sample from Piecewise Linear Fuzzy Numbers (PLFNs), with n PLFNs. This sample is an array with ` dim=c(knot.n+2,2,n) `.

## Value

This function returned a Piecewise Linear Fuzzy Number.

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```if(!require(FuzzyNumbers)){install.packages("FuzzyNumbers")} library(FuzzyNumbers) n=3; knot.n=4 Sample <- S.PLFN( n, knot.n, X.dist="norm", X.dist.par=c(3,2), slX.dist="exp", slX.dist.par=3, srX.dist="beta", srX.dist.par=c(1,3) ) Sample # For plotting random fuzzy sample: xlim = c(0, max(Sample[knot.n+2,2,])) plot( cuts.to.PLFN(Sample[,,1]), type="b", xlim=xlim ) plot( cuts.to.PLFN(Sample[,,2]), type="b", add=TRUE ) plot( cuts.to.PLFN(Sample[,,3]), type="b", add=TRUE ) abline( h=round((knot.n+1):0/(knot.n+1),4), lty=3, col="gray70") plot(Mean(Sample), col=4, lwd=2, add=TRUE, type="b") plot(Var(Sample), col=3, lwd=2, add=TRUE, type="b") S = Sd(Sample) S PLFN.to.cuts(S, knot.n) plot(S, col=2, lwd=2, add=TRUE, type="b") ```