norm.data

dt.ori

a matrix(2 x n) containing the range of
  the data, where n is the number of variables, and first
  and second rows are the minimum and maximum value,
  respectively.

range.data

min.scale

max.scale

This function is to transform from real-valued data into
  normalized data.

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.

norm.data: The data normalization

Description

Usage

Arguments

Value

See Also