R: GFS.Thrift model building

GFS.Thrift {frbs}

R Documentation

GFS.Thrift model building

Description

This is the internal function that implements the Thrift's technique based on a genetic algorithm. It is used to tackle regression tasks. Users do not need to call it directly, but just use frbs.learn and predict.

Usage

GFS.Thrift(data.train, popu.size = 10, num.labels, persen_cross = 0.6,
  persen_mutant = 0.3, max.gen = 10, range.data.ori,
  type.defuz = "WAM", type.tnorm = "MIN", type.snorm = "MAX",
  type.mf = "TRIANGLE", type.implication.func = "ZADEH")

Arguments

`data.train`	a matrix (`m \times n`) of normalized data for the training process, where `m` is the number of instances and `n` is the number of variables; the last column is the output variable. Note the data must be normalized between 0 and 1.
`popu.size`	the size of the population which is generated in each generation.
`num.labels`	a matrix describing the number of linguistic terms.
`persen_cross`	a real number between 0 and 1 representing the probability of crossover.
`persen_mutant`	a real number between 0 and 1 representing the probability of mutation.
`max.gen`	the maximal number of generations for the genetic algorithm.
`range.data.ori`	a matrix containing the ranges of the original data.
`type.defuz`	the type of the defuzzification method. For more detail, see `defuzzifier`. The default value is `WAM`.
`type.tnorm`	the type of t-norm. For more detail, please have a look at `inference`.
`type.snorm`	the type of s-norm. For more detail, please have a look at `inference`.
`type.mf`	the type of shape of membership function. See `fuzzifier`.
`type.implication.func`	the type of implication function. See `WM`.

Details

This method was developed by Thrift using Mamdani's model as fuzzy IF-THEN rules. In this method, we consider a table as a genotype with alleles that are fuzzy set indicators over the output domain. The phenotype is produced by the behavior produced by the fuzzification, max-* composition, and defuzzification operations. A chromosome (genotype) is formed from the decision table by going rowwise and producing a string of numbers from the code set. Standard crossover and mutation operators can act on these string.

References