This is the internal function that implements the model
proposed by L.X. Wang and J.M. Mendel. It is used to
solve regression task. Users do not need to call it
directly, but just use frbs.learn and
predict
a matrix(2 x n) containing the range of
the data, where n is the number of variables, and first
and second rows are the minimum and maximum values,
respectively.
data.train
a matrix(m x n) of data for the
training process, where m is the number of instances and
n is the number of variables; the last column is the
output variable.
num.labels
a matrix(1 x n), whose elements
represent the number of labels (fuzzy terms); n is the
number of variables.
type.mf
the type of the membership function. See
frbs.learn.
classification
a boolean representing whether it
is a classification problem or not.
Details
The fuzzy rule-based system for learning from L.X. Wang
and J.M. Mendel's paper is implemented in this function.
For the learning process, there are three stages: Step 1
divides the input and output spaces of the given
numerical data into fuzzy regions. Step 2 generates fuzzy
IF-THEN rules from the training data. Step 3 determines a
degree of each rule. In the prediction phase, there are
four steps: fuzzification, checking the rules, inference,
and defuzzification.
References
L. X. Wang and J.M. Mendel, "Generating fuzzy rule by
learning from examples", IEEE Trans. Syst., Man, and
Cybern., vol. 22, no. 6, pp. 1414 - 1427 (1992).