frbsObjectFactory: The object factory for frbs objects
In frbs: Fuzzy Rule-Based Systems for Classification and Regression Tasks

Description Usage Arguments Details Value

This function creates objects of type frbs. Currently, its implementation is very basic and does no argument checking, as it is only used internally.

1	frbsObjectFactory(mod)

mod

a list containing all the attributes for the object

The members of the frbs object depend on the used learning method. The following list describes all of the members that can be present.

num.labels: the number of linguistic terms for the variables
varout.mf: a matrix to generate the shapes of the membership functions for the output variable. The first row represents the shape of the membership functions, the other rows contain the parameters that have been generated. Whether the values of parameters within the matrix are normalized to lie between 0 and 1 or not depends on the selected method.
rule: the fuzzy IF-THEN rules; In the GFS.FR.MOGUL case, a rule refers to the parameter values of the membership function which represents the rule.
rule.data.num: the fuzzy IF-THEN rules in integer format.
varinp.mf: a matrix to generate the shapes of the membership functions for the input variables. The first row represents the shape of the membership functions, the other rows contain the non NA values representing the parameters related with their type of membership function. For example, TRAPEZOID, TRIANGLE, and GAUSSIAN have four, three, and two values as their parameters, respectively. Whether the values of parameters within the matrix are normalized to lie between 0 and 1 or not depends on the selected method.
type.model: the type of model. Here, MAMDANI refers to the Mamdani model, and TSK refers to the Takagi Sugeno Kang model on the consequence part.
func.tsk: a matrix of the Takagi Sugeno Kang model consequent part of the fuzzy IF-THEN rules.
class: a matrix representing classes of FRBCS model
num.labels: a number of linguistic terms on each variables/attributes.
type.defuz: the type of the defuzzification method.
type.tnorm: the type of the t-norm method.
type.snorm: the type of the s-norm method.
type.mf: the type of shapes of membership functions.
type.implication.func: the type of the implication function.
method.type: the type of the selected method.
name: the name given to the model.
range.data.ori: range of the original data (before normalization).
cls: cluster centers.
Dthr: the boundary parameter of the DENFIS method.
d: the multiplier parameters of the DENFIS method.
r.a: the neighborhood factor of SBC.
degree.rule: certainty degree of rules.
rule.data.num: a matrix representing the rules in integer form.
grade.cert: grade of certainty for classification problems.
alpha.heuristic: a parameter for the heuristic of the FS.HGD method.
var.mf.tune: a matrix of parameters of membership function for lateral tuning.
mode.tuning: a type of lateral tuning.
rule.selection: a boolean of rule selection.
colnames.var: the names of variables.