DM.update

a matrix(m x n) of training data, 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 containing the rulebase.
  Its elements are integers, see <code><a href="/link/rulebase?package=frbs&version=1.0-0" rd-options="" data-mini-rdoc="frbs::rulebase">rulebase</a></code>.

rule.data.num

a matrix with the degrees 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 of parameters of the functions
  on the consequent part of the Takagi Sugeno Kang model.

func.tsk

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

varinp.mf

the step size of the descent method,
  between 0 and 1.

step.size

a matrix which is obtained from the
  defuzzification. Please have a look at
  <code><a href="/link/defuzzifier?package=frbs&version=1.0-0" rd-options="" data-mini-rdoc="frbs::defuzzifier">defuzzifier</a></code>.

The role of this function is to update the parameters of
  the DM method. This function is called by the main
  function of the DM method, <code><a href="/link/DM?package=frbs&version=1.0-0" rd-options="" data-mini-rdoc="frbs::DM">DM</a></code>.

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.

DM.update: DM updating function

Description

Usage

Arguments

See Also