Fuzzy Rule-based Systems for Classification and Regression Tasks
Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and
Jose Manuel Benitez
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.
'FRBS.MainFunction.R' 'FSpacePartition.Method.R'
'FSpacePartition.FunctionCollection.R'
'FSpacePartition.Predict.R' 'FNN.FunctionCollection.R'
'FNN.Methods.R' 'FCluster.Methods.R'
'FCluster.FunctionCollection.R' 'FCluster.Predict.R'
'FGradDescent.Methods.R' 'FGradDescent.FunctionCollection.R'
'GFS.Methods.R' 'GFS.FunctionCollection.R' 'GFS.Predict.R'
'docData.R' 'frbs-package.R'

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.