GaussianMF-class: GaussianMF Membership Function S4 class

In anfis: Adaptive Neuro Fuzzy Inference System in R

Description

Represent a concrete GaussianMF shaped Membership Function S4 class with parameters mu, sigma. Slots inherited of MembershipFunction class and related functions: show, print, derivateMF, evaluateMF, [ and [<-.

Slots

parameters

named numeric vector with parameters of Membership Function.

nParameters

integer with the number of parameters for validity check.

name

character The description of the membership function.

expression

expression object just to display purposes.

Note

derivateMF, evaluateMF are extended. Prototype is defined and validity is inherited.

Author(s)

Cristobal Fresno [e-mail], Andrea S. Llera [e-mail] and Elmer A. Fernandez [e-mail]

See Also

BellMF-class and NormalizedGaussianMF-class

Other Membership Functions: BellMF, BellMF-class; MembershipFunction, MembershipFunction-class; NormalizedGaussianMF, NormalizedGaussianMF-class; [,MembershipFunction-method, [<-,MembershipFunction-method, extract-methods, extract-methods; derivateMF, derivateMF, derivateMF, derivateMF, derivateMF, derivateMF,BellMF-method, derivateMF,GaussianMF-method, derivateMF,MembershipFunction-method, derivateMF,NormalizedGaussianMF-method, derivateMF-methods; evaluateMF, evaluateMF, evaluateMF, evaluateMF, evaluateMF, evaluateMF,BellMF-method, evaluateMF,GaussianMF-method, evaluateMF,MembershipFunction-method, evaluateMF,NormalizedGaussianMF-method, evaluateMF-methods; print,MembershipFunction-method; show,MembershipFunction-method

Examples

#GaussianMF example I
#A Gaussian membership function with default prototype (mu=0, sigma=1)
#The membership of x in the Gaussian, should be 1/sqrt(2*pi) = 0.3989423
#The derivate of the first parameter at x, should be 0
#The derivate on "mu" parameter at x, should be 0
gaussian <- new(Class="GaussianMF")
gaussian
evaluateMF(object=gaussian, x=0)
derivateMF(object=gaussian, x=0, i=1)
derivateMF(object=gaussian, x=0, i="mu")
#
#GaussianMF example II
#A Gaussian membership function with parameters (mu=0, sigma=1)
#The membership of x in the Gaussian, should be 1/sqrt(2*pi) = 0.3989423
#The derivate of the first parameter at x, should be 0
#The derivate on "mu" parameter at x, should be 0
gaussian2 <- new(Class="GaussianMF",parameters=c(mu=0,sigma=1))
gaussian2
evaluateMF(object=gaussian2, x=0)
derivateMF(object=gaussian2, x=0, i=1)
derivateMF(object=gaussian2, x=0, i="mu")

Example output

Loading required package: parallel
MembershipFunction:  Gaussian Membership Function 
Number of parameters: 2 
   mu sigma 
    0     1 
Expression: expression(1/sqrt(2 * pi * sigma^2) * exp(-1/2 * ((x - mu)/sigma)^2))
    sigma 
0.3989423 
sigma 
    0 
sigma 
    0 
MembershipFunction:  Gaussian Membership Function 
Number of parameters: 2 
   mu sigma 
    0     1 
Expression: expression(1/sqrt(2 * pi * sigma^2) * exp(-1/2 * ((x - mu)/sigma)^2))
    sigma 
0.3989423 
sigma 
    0 
sigma 
    0 

anfis documentation built on May 2, 2019, 2:38 a.m.