Description
This package implements functionality and various
algorithms to build and use fuzzy rule-based systems (FRBS).
FRBSs are based on the fuzzy concept, 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, robotics, and speech
recognition. 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, least squares methods, gradient descent, etc. This
package aims to implement the most widely used standard
procedures, thus offering a standard package for FRBS modeling
to the R community.
