This is the internal function that implements the hybrid
neural fuzzy inference system (HyFIS). 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
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.
max.iter
the maximal number of iterations.
range.data.ori
a matrix containing the ranges of
the original data.
step.size
step size of the gradient descent
method.
Details
This method was proposed by J. Kim and N. Kasabov. There
are two phases in this method for learning, namely the
knowledge acquisition module and the structure and
parameter learning. The knowledge acquition module uses
the techniques of Wang and Mendel. The learning of
structure and parameters is a supervised learning method
using gradient descent-based learning algorithms. This
function generates a model which consists of a rule
database and parameters of the membership functions. The
rules of HyFIS use the Mamdani model on the antecedent
and consequent parts. Futhermore, HyFIS uses a Gaussian
membership function. So, there are two kinds of
parameters that are optimized, mean and variance of the
Gaussian function.
References
J. Kim and N. Kasabov, "HyFIS: adaptive neuro-fuzzy
inference systems and their application to nonlinear
dynamical systems", Neural Networks 12, 1301 - 1319
(1999).