DENFIS.eng

the frbs model. See
  <code><a href="/link/frbs-object?package=frbs&version=1.0-0" 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 an internal function for the prediction
  phase using the DENFIS method. The user should use this
  function not directly, but with calling
  <code><a href="/link/predict?package=frbs&version=1.0-0" rd-options="" data-mini-rdoc="frbs::predict">predict</a></code>.

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.

DENFIS.eng: DENFIS prediction function

Description

Usage

Arguments

Value

See Also