Description: 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.

Package: frbs
Maintainer: Christoph Bergmeir <c.bergmeir@decsai.ugr.es>
License: GPL (>= 2) | file LICENSE
Title: Fuzzy Rule-based Systems for Classification and Regression Tasks
Author: Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and
Jose Manuel Benitez
Description: 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. 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.
Version: 3.0-0
URL: http://sci2s.ugr.es/dicits/software/FRBS
Date: 2013-02-26
Suggests: class, e1071, XML
NeedsCompilation: no
Packaged: 2015-01-15 20:19:38 UTC; Lala
Repository: CRAN
Date/Publication: 2015-01-16 06:42:32