ANFIS.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

the range of the input variables, as a
  matrix(2 x n).

range.input

the range of the output variable, as
  a matrix(2 x n).

range.output

a matrix containing the rule base in
  integer form.

rule.data.num

a matrix with the degrees of rules. See
  <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 function on
  the consequent part using the Takagi Sugeno Kang model.

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

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

ANFIS.update: ANFIS updating function

Description

Usage

Arguments

See Also