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lfl (version 1.1)

Linguistic Fuzzy Logic

Description

Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association rules, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE).

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Version

Install

install.packages('lfl')

Monthly Downloads

283

Version

1.1

License

GPL (>= 3.0)

Maintainer

Michal Burda

Last Published

June 3rd, 2015

Functions in lfl (1.1)

Return vector of values from given interval

Print an instance of the farules class

Test whether x is a valid object of the fsets class

Fuzzy Rule-Based Ensemble (FRBE) of time-series forecasts

as.data.frame.farules

Convert the 'farules' object into a data frame

Return the first part of an instance of the fsets class

Extract consequent-part (RHS) of the rules in a list

Plot a 'fsets' object

Transform data into a set of linguistic fuzzy attributes

Compute rule base coverage of data

Print an instance of the frbe class

Evaluate the performance of the FRBE forecast

Compute forecast errors

Reduce the size of rule base

Return the first part of an instance of the farules class

Test whether x is a valid object of the frbe class

Implicational aggregation of rules' consequents into a fuzzy set

Searching for fuzzy association rules

as.matrix.fsets

Convert a 'fsets' object into matrix

Perform a Perception-based Logical Deduction (PbLD) with given rule-base on given dataset

Return the last part of an instance of the farules class

Convert fuzzy set into a crisp numeric value

Select several rows and columns from a data object

Determine whether the first set of predicates is more specific (or equal) than the other.

Linguistic Fuzzy Logic

Extract antecedent-part (LHS) of the rules in a list

Print an instance of the fsets class

A class of rules with statistical characteristics.

Return the last part of an instance of the fsets class

Compute membership degrees of values to the fuzzy set

Triangular norm

Combine several 'fsets' objects into a single one

A class of a table with several fuzzy sets.

Test whether x is a valid object of the farules class

Transform data into a set of fuzzy attributes using triangular or raised cosine shapes of the fuzzy sets

Compute truth-degrees of rules on data

From a set of rules, remove each rule for which another rule exists that is more specific.