HGD.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

a matrix with the degrees of rules which
  is the 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 of parameters of the function on
  the consequent part using the Takagi Sugeno Kang model.
  See <code><a href="/link/rulebase?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::rulebase">rulebase</a></code>.

func.tsk

a matrix of parameters of membership
  functions of the input variables.

varinp.mf

a real number between 0 and 1
  representing the step size of the gradient descent.

step.size

a matrix which is obtained by the
  <code><a href="/link/defuzzifier?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::defuzzifier">defuzzifier</a></code>.

The role of this function is to update parameters within
  the simplified TSK fuzzy rule generation method using
  heuristics and the gradient descent method (FS.HGD). This
  function is called by the main function of the FS.HGD
  method, see <code><a href="/link/FS.HGD?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::FS.HGD">FS.HGD</a></code>.

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.

HGD.update: FS.HGD updating function

Description

Usage

Arguments

See Also