frbs3.2-0 package

Fuzzy Rule-Based Systems for Classification and Regression Tasks

ANFIS model building

ANFIS updating function

A data generator

Defuzzifier to transform from linguistic terms to crisp values

DENFIS prediction function

DENFIS model building

The data de-normalization

FIR.DM updating function

Evolving Clustering Method

FH.GBML model building

FIR.DM model building

FRBCS.CHI model building

FRBCS: prediction phase

FRBCS.W model building

Getting started with the frbs package

The prediction phase

The frbs model generator

The frbs model building function

The object factory for frbs objects

The frbsPMML generator

FS.HGD model building

Transforming from crisp set into linguistic terms

GFS.FR.MOGUL model building

GFS.FR.MOGUL: The prediction phase

GFS.GCCL.test: The prediction phase

GFS.GCCL model building

GFS.LT.RS model building

GFS.LT.RS: The prediction phase

GFS.Thrift model building

GFS.Thrift: The prediction phase

FS.HGD updating function

HyFIS model building

HyFIS updating function

The process of fuzzy reasoning

The data normalization

The plotting function

The frbs prediction stage

The frbsPMML reader

The rule checking function

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

SBC prediction phase

SLAVE model building

SLAVE.test: The prediction phase

The summary function for frbs objects

WM model building

The frbsPMML writer

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.

Maintainer: Christoph Bergmeir License: GPL (>= 2) | file LICENSE Last published: 2019-12-15