GFS.GCCL.eng

the <code><a href="/link/frbs-object?package=frbs&version=2.0-0" rd-options="" data-mini-rdoc="frbs::frbs-object">frbs-object</a></code>.

object

a matrix(m x n) of data for the prediction
  process, where m is the number of instances and n is the
  number of input variables.

newdata

This function is the internal function of the GFS.GCCL
  and FH.GBML method to compute the predicted values.

This package implements functionality and various
        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
        FRBS modeling to the R community.

GFS.GCCL.eng: GFS.GCCL.test: The prediction phase

Description

Usage

Arguments

Value