This is the internal function that implements the
simplified TSK fuzzy rule generation method using
heuristics and gradient descent method (FS.HGD). It is
used to solve regression tasks. Users do not need to call
it directly, but just use frbs.learn and
predict.
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.
range.data
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.
num.labels
a matrix(1 x n), whose elements
represent the number of labels (fuzzy terms); n is the
number of variables.
max.iter
maximal number of iterations.
step.size
step size of the descent method.
alpha.heuristic
a positive real number which is
the heuristic parameter.
Details
This method was proposed by Ken Nozaki, H. Ishibuchi, and
Hideo Tanaka. It uses fuzzy IF-THEN rules with nonfuzzy
singletons (i.e. real numbers) in the consequent parts.
The techniques of space partition are implemented to
generate the antecedent part, while the initial
consequent part of each rule is determined by the
weighted mean value of the given training data. Then, the
gradient descent method updates the value of the
consequent part. Futhermore, the heuristic value given by
the user affects the value of weight of each data.
References
H. Ishibuchi, K. Nozaki, H. Tanaka, Y. Hosaka and M.
Matsuda, "Empirical study on learning in fuzzy systems by
rice taste analysis", Fuzzy Set and Systems, vol. 64, no.
2, pp. 129 - 144 (1994).