Title: Implementation of the "Flexible kernels for RBF network"
Description: Flexible kernerl for radial basis function network

Packaged: Tue Sep 15 23:45:51 2009; root

Author: Lala Septem Riza, Christoph Bergmeir, Francisco Herrera
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
Collate: 'ANFIS.R' 'ANFIS.update.R' 'convert.MF.R' 'convert.rule.R'
'denorm.data.R' 'DM.R' 'DM.update.R' 'docData.R' 'ECM.R'
'fuzzifier.R' 'GFS.R' 'HGD.R' 'HGD.update.R' 'HyFIS.R'
'rulebase.R' 'SBC.R' 'SBC.test.R' 'summary.R' 'WM.R' 'MSGFS.R'

Title: Fuzzy Rule-based Systems for Classification and Regression Tasks
Author: Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and
algorithms to build and use fuzzy rule-based systems (FRBSs).
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. This package aims to implement the most widely
used standard procedures, thus offering a standard package for