Description Usage Arguments Details Value See Also
View source: R/FSpacePartition.FunctionCollection.R
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 IF-THEN 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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.