# frbs v3.2-0

Monthly downloads

## Fuzzy Rule-Based Systems for Classification and Regression Tasks

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.

## Functions in frbs

Name | Description | |

GFS.GCCL | GFS.GCCL model building | |

GFS.Thrift.test | GFS.Thrift: The prediction phase | |

GFS.Thrift | GFS.Thrift model building | |

FRBCS.eng | FRBCS: prediction phase | |

HyFIS | HyFIS model building | |

FS.HGD | FS.HGD model building | |

HGD.update | FS.HGD updating function | |

GFS.GCCL.eng | GFS.GCCL.test: The prediction phase | |

ECM | Evolving Clustering Method | |

SLAVE.test | SLAVE.test: The prediction phase | |

GFS.FR.MOGUL | GFS.FR.MOGUL model building | |

GFS.FR.MOGUL.test | GFS.FR.MOGUL: The prediction phase | |

WM | WM model building | |

SBC.test | SBC prediction phase | |

SLAVE | SLAVE model building | |

GFS.LT.RS | GFS.LT.RS model building | |

data.gen3d | A data generator | |

frbs.gen | The frbs model generator | |

frbs.eng | The prediction phase | |

write.frbsPMML | The frbsPMML writer | |

defuzzifier | Defuzzifier to transform from linguistic terms to crisp values | |

plotMF | The plotting function | |

frbsPMML | The frbsPMML generator | |

frbsObjectFactory | The object factory for frbs objects | |

norm.data | The data normalization | |

frbsData | Data set of the package | |

frbs.learn | The frbs model building function | |

GFS.LT.RS.test | GFS.LT.RS: The prediction phase | |

rulebase | The rule checking function | |

SBC | The subtractive clustering and fuzzy c-means (SBC) model building | |

HyFIS.update | HyFIS updating function | |

summary.frbs | The summary function for frbs objects | |

frbs-package | Getting started with the frbs package | |

denorm.data | The data de-normalization | |

fuzzifier | Transforming from crisp set into linguistic terms | |

inference | The process of fuzzy reasoning | |

read.frbsPMML | The frbsPMML reader | |

predict.frbs | The frbs prediction stage | |

FIR.DM | FIR.DM model building | |

FH.GBML | FH.GBML model building | |

ANFIS | ANFIS model building | |

DM.update | FIR.DM updating function | |

DENFIS.eng | DENFIS prediction function | |

DENFIS | DENFIS model building | |

ANFIS.update | ANFIS updating function | |

FRBCS.CHI | FRBCS.CHI model building | |

FRBCS.W | FRBCS.W model building | |

No Results! |

## Vignettes of frbs

## Last month downloads

## Details

License | GPL (>= 2) | file LICENSE |

URL | http://sci2s.ugr.es/dicits/software/FRBS |

Date | 2019-12-15 |

VignetteBuilder | R.rsp |

RoxygenNote | 6.1.0 |

NeedsCompilation | no |

Packaged | 2019-12-15 01:48:46 UTC; bergmeir |

Repository | CRAN |

Date/Publication | 2019-12-15 06:10:02 UTC |

suggests | class , e1071 , R.rsp , XML |

Contributors | Christoph Bergmeir, José Benítez, Lala Riza, Francisco Herrera |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/frbs)](http://www.rdocumentation.org/packages/frbs)
```