This is the internal function that implements the fuzzy
inference rules by descent method (FIR.DM). It is used to
solve regression tasks. 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 normalized data, where n is the number of variables,
and first and second rows are the minimum and maximum
value, respectively.
data.train
a matrix(m x n) of data for training,
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), where n is
the number of variables.
max.iter
the maximal number of iterations.
step.size
the step size of the descent method,
between 0 and 1.
Details
This method was proposed by Hiroyoshi Nomura, Isao
Hayashi, and Noboru Wakami. FIR.DM uses simplified fuzzy
reasoning where the consequent part is a real number (a
particular case within the Takagi Sugeno Kang model),
while the membership function on the antecedent part is
expressed by an isosceles triangle. So, in the learning
phase, FIR.DM updates three parameters which are center
and width of the triangular and a real number on the
consequent part using a descent method.
References
H. Nomura, I. Hayashi and N. Wakami, "A learning method
of fuzzy inference rules by descent method", IEEE
International Conference on Fuzzy Systems, pp. 203 - 210
(1992).