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lfl (version 1.0)

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

435

Version

1.0

License

GPL (>= 3.0)

Maintainer

Michal Burda

Last Published

January 15th, 2015

Functions in lfl (1.0)

as.matrix.fsets

Convert a 'fsets' object into matrix

Implicational aggregation of rules' consequents into a fuzzy set

Transform data into a set of linguistic fuzzy attributes

Test whether x is a valid object of the frbe class

Return vector of values from given interval

Select several rows and columns from a data object

Compute membership degrees of values to the fuzzy set

Convert fuzzy set into a crisp numeric value

Print an instance of the fsets class

Test whether x is a valid object of the fsets class

Print an instance of the farules class

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

Print an instance of the frbe class

Return the last part of an instance of the farules class

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

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

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

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

Linguistic Fuzzy Logic

Return the first part of an instance of the fsets class

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

Compute truth-degrees of rules on data

Return the last part of an instance of the fsets class

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

A class of rules with statistical characteristics.

Compute rule base coverage of data

Return the first part of an instance of the farules class

Combine several 'fsets' objects into a single one

Evaluate the performance of the FRBE forecast

Test whether x is a valid object of the farules class

A class of a table with several fuzzy sets.

Triangular norm

Searching for fuzzy association rules

Reduce the size of rule base

Compute forecast errors