frbs (version 1.0-0)

MSGFS: MSGFS model building

Description

This is the internal function that implements the multi-stage genetic fuzzy systems (MSGFS) based on iterative rule learning approach. Users do not need to call it directly, but just use frbs.learn and predict.

Usage

MSGFS(data.train, popu.size, persen_cross, persen_mutant,
    max.iter, range.data.ori, epsilon)

Arguments

data.train

a matrix(m x n) of 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.

popu.size

the size of the population which is generated in each generation.

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.iter

the maximal number of iterations.

range.data.ori

a matrix containing the ranges of the original data.

epsilon

a real number between 0 and 1 representing the boundary of covering factor.

Details

This method was proposed by Herrera et al. MSGFS implements a genetic algorithm in order to determine the structure of the fuzzy IF-THEN rules and the membership function parameters. There are two general types of fuzzy IF-THEN rules, namely the descriptive and the approximative/free semantic approaches. A descriptive approach means that the linguistic labels represent a real-world semantic; the linguistic labels are uniformly defined for all rules. In contrast, in the approximative approach there isn't any associated linguistic label. This method is based on the latter one. We model a fuzzy IF-THEN rule on a chromosome which consists of the parameter values of the membership function. So, every rule has its own membership function values. A population contains many such generated chromosomes, based on the iterative rule learning approach (IRL). IRL means that the chromosomes will be generated one by one, taking into account the fitness value and covering factor, until there are sufficient chromosomes in the population. After having obtained the population, the genetic algorithm is started, using the genetic operators selection, mutation, and crossover.

References

Antonio Gonzalez and Francisco Herrera, "Multi-stage genetic fuzzy systems based on the iterative rule learning approach," Mathware & Soft Computing 4, pp. 233 - 249 (1997).

F. Herrera, M. Lozano, J.L. Verdegay, "Tuning fuzzy logic controllers by genetic algorithms," Internat. J. Approx. Reasoning 12, pp. 299 - 315 (1995).

F. Herrera, M. Lozano, J.L. Verdegay, "Generating rules from examples using genetic algorithms," In: B. Bounchon, R. Yager, L. Zadeh (Eds.), Fuzzy Logic and Soft Computing, Word Scientific, pp. 11 - 20 (1995).

F. Herrera, M. Lozano, and J.L. Verdegay, "A learning process for fuzzy control rules using genetic algorithms", Fuzzy Sets and Systems, 100, pp. 143 - 158 (1998).

O. Cordon, M.J. del Jesus, F. Herrera, M. Lozano, "MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach," International Journal of Intelligent Systems, vol. 14, pp. 1123 - 1153 (1999).

See Also

MSGFS.test, frbs.learn, and predict