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frbs
View on CRAN: Click
here
Download and install frbs package within the R console
Install from CRAN:
install.packages("frbs")
Install from Github:
library("remotes")
install_github("cran/frbs")
Install by package version:
library("remotes")
install_version("frbs", "3.2-0")
Attach the package and use:
library("frbs")
Maintained by
Christoph Bergmeir
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.frbs . frbs citation info . frbs results . frbs.pdf . frbs: Fuzzy Rule-based Systems for Classification and Regression in R . frbsPMML: A Universal Representation Framework for Fuzzy Rule-Based Systems Based on PMML . frbs_3.2-0.tar.gz . frbs_3.2-0.zip . frbs_3.2-0.zip . frbs_3.2-0.zip . frbs_3.2-0.tgz . frbs_3.2-0.tgz . frbs_3.2-0.tgz . frbs_3.2-0.tgz . frbs_3.2-0.tgz . frbs_3.2-0.tgz . frbs archive . https://CRAN.R-project.org/package=frbs .
First Published: 2012-10-29
Latest Update: 2019-12-15
Description:
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.
How to cite:
Christoph Bergmeir (2012). frbs: Fuzzy Rule-Based Systems for Classification and Regression Tasks. R package version 3.2-0, https://cran.r-project.org/web/packages/frbs. Accessed 01 Mar. 2025.
Previous versions and publish date:
Other packages that cited frbs R package
View frbs citation profile
Other R packages that frbs depends,
imports, suggests or enhances
Complete documentation for frbs
Functions, R codes and Examples using
the frbs R package
Some associated functions: ANFIS . ANFIS.update . DENFIS.eng . DENFIS . DM.update . ECM . FH.GBML . FIR.DM . FRBCS.CHI . FRBCS.W . FRBCS.eng . FS.HGD . GFS.FR.MOGUL . GFS.FR.MOGUL.test . GFS.GCCL.eng . GFS.GCCL . GFS.LT.RS . GFS.LT.RS.test . GFS.Thrift . GFS.Thrift.test . HGD.update . HyFIS . HyFIS.update . SBC . SBC.test . SLAVE . SLAVE.test . WM . data.gen3d . defuzzifier . denorm.data . frbs-package . frbs.eng . frbs.gen . frbs.learn . frbsData . frbsObjectFactory . frbsPMML . fuzzifier . inference . norm.data . plotMF . predict.frbs . read.frbsPMML . rulebase . summary.frbs . write.frbsPMML .
Some associated R codes: FCluster.FunctionCollection.R . FCluster.Methods.R . FCluster.Predict.R . FGradDescent.FunctionCollection.R . FGradDescent.Methods.R . FNN.FunctionCollection.R . FNN.Methods.R . FRBS.MainFunction.R . FRBS.pmml.R . FSpacePartition.FunctionCollection.R . FSpacePartition.Method.R . FSpacePartition.Predict.R . GFS.FunctionCollection.R . GFS.Methods.R . GFS.Predict.R . docData.R . frbs-package.R . pmml.R . pmml.frbs.R . Full frbs package functions and examples
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