HGD.update

a matrix (\(m \times n\)) of normalized 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 rd-options="" href="/link/inference?package=frbs&version=3.2-0" 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 rd-options="" href="/link/rulebase?package=frbs&version=3.2-0" 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 rd-options="" href="/link/defuzzifier?package=frbs&version=3.2-0" 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 rd-options="" href="/link/FS.HGD?package=frbs&version=3.2-0" data-mini-rdoc="frbs::FS.HGD">FS.HGD</a></code>.

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts.
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. Furthermore, in this
version we provide a universal framework named 'frbsPMML', which is adopted
from the Predictive Model Markup Language (PMML), for representing FRBS
models. PMML is an XML-based language to provide a standard for describing
models produced by data mining and machine learning algorithms. Therefore,
we are allowed to export and import an FRBS model to/from 'frbsPMML'.
Finally, this package aims to implement the most widely used standard
procedures, thus offering a standard package for FRBS modeling to the R
community.

Christoph Bergmeir

frbs

Fuzzy Rule-Based Systems for Classification and Regression Tasks

HGD.update function

a matrix with the degrees of rules which is the result of the <code><a rd-options='' href='inference'>inference</a></code>.

a matrix of parameters of the function on the consequent part using the Takagi Sugeno Kang model. See <code><a rd-options='' href='rulebase'>rulebase</a></code>.

a matrix which is obtained by the <code><a rd-options='' href='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 rd-options='' href='FS.HGD'>FS.HGD</a></code>.

HGD.update: FS.HGD updating function

Description

Usage

Arguments

See Also