HGD.update: FS.HGD updating function

Description Usage Arguments See Also

View source: R/FGradDescent.FunctionCollection.R

Description

The role of this function is to update parameters within the simplified TSK fuzzy rule generation method using heuristics and the gradient descent method (FS.HGD). This function is called by the main function of the FS.HGD method, see FS.HGD.

Usage

1
HGD.update(data.train, miu.rule, func.tsk, varinp.mf, step.size = 0.01, def)

Arguments

data.train

a matrix (m \times n) of normalized data for the training process, where m is the number of instances and n is the number of variables; the last column is the output variable.

miu.rule

a matrix with the degrees of rules which is the result of the inference.

func.tsk

a matrix of parameters of the function on the consequent part using the Takagi Sugeno Kang model. See rulebase.

varinp.mf

a matrix of parameters of membership functions of the input variables.

step.size

a real number between 0 and 1 representing the step size of the gradient descent.

def

a matrix which is obtained by the defuzzifier.

See Also

FS.HGD



frbs documentation built on May 19, 2017, 7:28 p.m.
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