HyFIS.update

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.

data.train

matrix of defuzzification results. See
  <code><a href="/link/defuzzifier?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::defuzzifier">defuzzifier</a></code>.

fuzzy IF-THEN rules. See
  <code><a href="/link/rulebase?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::rulebase">rulebase</a></code>.

rule

a list of names of the output
  variable.

names.varoutput

a matrix of parameters of the membership
  functions. Please see <code><a href="/link/fuzzifier?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::fuzzifier">fuzzifier</a></code>.

var.mf

a matrix of degree of rules which is a
  result of the <code><a href="/link/inference?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::inference">inference</a></code>.

miu.rule

a matrix(1 x n) whose elements
  represent the number of labels (or fuzzy terms), where n
  is the number of variables.

num.labels

a matrix of parameters of the membership
  functions which is a result of the
  <code><a href="/link/fuzzifier?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::fuzzifier">fuzzifier</a></code>.

a real number, the step size of the
  gradient descent.

step.size

a matrix of degrees of rules. See
  <code><a href="/link/frbs-object?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::frbs-object">frbs-object</a></code>.

degree.rule

This function is called by <code><a href="/link/HyFIS?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::HyFIS">HyFIS</a></code> to update
  the parameters within the HyFIS method.

This package implements functionality and various
        algorithms to build and use fuzzy rule-based systems (FRBSs).
        FRBSs are based on the concept of fuzzy sets, proposed by Zadeh
        in 1965, which aims at representing the reasoning of human
        experts in a set of IF-THEN rules, to handle real-life problems
        in, e.g., control, prediction and inference, data mining,
        bioinformatics data processing, and robotics. FRBSs are also
        known as fuzzy inference systems and fuzzy models. During the
        modeling of an FRBS, there are two important steps that need to
        be conducted: structure identification and parameter
        estimation. Nowadays, there exists a wide variety of algorithms
        to generate fuzzy IF-THEN rules automatically from numerical
        data, covering both steps. Approaches that have been used in
        the past are, e.g., heuristic procedures, neuro-fuzzy
        techniques, clustering methods, genetic algorithms, squares
        methods, etc. This package aims to implement the most widely
        used standard procedures, thus offering a standard package for
        FRBS modeling to the R community.

HyFIS.update: HyFIS updating function

Description

Usage

Arguments

See Also