This function creates objects of type frbs
. Currently, its
implementation is very basic and does no argument checking, as
it is only used internally.
frbsObjectFactory(mod)
a list containing all the attributes for the object
an object of type frbs
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.