a matrix(m x n) of data, where m is the
number of instances and n is the number of variables.
rule
a list or matrix of fuzzy IF-THEN rules, as
discussed in rulebase.
range.output
a matrix(2 x n) containing the range
of the output data.
names.varoutput
a list for giving names to the
fuzzy terms. See rulebase.
varout.mf
a matrix for constructing the membership
function of the output variable. See
fuzzifier.
miu.rule
the results of the inference module. See
inference.
type.defuz
the type of defuzzification to be used,
where 1 means weighted average method, and 2, 3, 4 and 5
mean first, last, mean maxima and modified COG,
respectively.
type.model
the type of the model that will be used
in the simulation. Here, 1 or 2 means we use Mamdani or
Takagi Sugeno Kang (which includes the possibility for a
constant value), respectively.
func.tsk
a matrix used to build the linear
equation for the consequent part if we are using Takagi
Sugeno Kang. See also rulebase.
Value
A matrix of crisp values
Details
In this function, there exist two kinds of models which
are based on the Mamdani model and the Takagi Sugeno Kang
model on the consequent parts. For the Mamdani model
there are five methods for defuzzifying a fuzzy term A of
a universe of discourse Z. They are as follows: