DM.update

a matrix (\(m \times n\)) of normalized 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 rd-options="" href="/link/rulebase?package=frbs&version=3.1-0" 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 rd-options="" href="/link/inference?package=frbs&version=3.1-0" 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 rd-options="" href="/link/defuzzifier?package=frbs&version=3.1-0" data-mini-rdoc="frbs::defuzzifier">defuzzifier</a></code>.

The role of this function is to update the parameters of the fuzzy inference rules by descent method (FIR.DM).
This function is called by the main function of the FIR.DM method, <code><a rd-options="" href="/link/FIR.DM?package=frbs&version=3.1-0" data-mini-rdoc="frbs::FIR.DM">FIR.DM</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

DM.update function

a matrix containing the rulebase. Its elements are integers, see <code><a rd-options='' href='rulebase'>rulebase</a></code>.

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

a matrix which is obtained from the defuzzification. Please have a look at <code><a rd-options='' href='defuzzifier'>defuzzifier</a></code>.

The role of this function is to update the parameters of the fuzzy inference rules by descent method (FIR.DM).
This function is called by the main function of the FIR.DM method, <code><a rd-options='' href='FIR.DM'>FIR.DM</a></code>.

DM.update: FIR.DM updating function

Description

Usage

Arguments

See Also