Defuzzification is a transformation that extracts the crisp values from the linguistic terms.
1 2 3 
data 
a matrix (m \times n) of data, where m is the number of instances and n is the number of variables. 
rule 
a list or matrix of fuzzy IFTHEN rules, as discussed in 
range.output 
a matrix (2 \times n) containing the range of the output data. 
names.varoutput 
a list for giving names to the linguistic terms. See 
varout.mf 
a matrix constructing the membership function of the output variable.
See 
miu.rule 
the results of the inference module. See 
type.defuz 
the type of defuzzification to be used as follows.

type.model 
the type of the model that will be used in the simulation.
Here, 
func.tsk 
a matrix used to build the linear equation for the consequent part
if we are using Takagi Sugeno Kang. See also 
In this function, there exist two kinds of models which are based on the Mamdani and Takagi Sugeno Kang model. For the Mamdani model there are five methods for defuzzifying a linguistic term A of a universe of discourse Z. They are as follows:
weighted average method (WAM
).
first of maxima (FIRST.MAX
).
last of maxima (LAST.MAX
)
mean of maxima (MEAN.MAX
).
modified center of gravity (COG
).
A matrix of crisp values
fuzzifier
, rulebase
, and inference
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