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=1.0-0" rd-options="" data-mini-rdoc="frbs::defuzzifier">defuzzifier</a></code>.

fuzzy IF-THEN rules. See
  <code><a href="/link/rulebase?package=frbs&version=1.0-0" 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=1.0-0" 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=1.0-0" 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=1.0-0" 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=1.0-0" 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=1.0-0" 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 (FRBS).
        FRBSs are based on the fuzzy concept, 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, robotics, and speech
        recognition. 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, least squares methods, gradient descent, 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