Resumen

CASTRO, MARCO ANTONIO  y  HERRERA, FRANCISCO. FINDING FUZZY IDENTIFICATION SYSTEM PARAMETERS USING A NEW DYNAMIC MIGRATION PERIOD-BASED DISTRIBUTED GENETIC ALGORITHM. Dyna rev.fac.nac.minas [online]. 2009, vol.76, n.159, pp.77-83. ISSN 0012-7353.

This paper presents a distributed genetic algorithm with dynamic determination of the migration period. The algorithm is especially well suited for the on line estimation of a fuzzy identification system parameters, using heterogeneous clusters. The results of the optimization of a TSK (Takagi-Sugeno-Kang) system for the identification of a biotechnological (fermentative) process including the solution’s quality and speedup analysis are presented. Comparative results using static and dynamic migration periods on the genetic algorithm are also presented.

Palabras clave : on-line identification; Takagi-Sugeno-Kang fuzzy model; distributed genetic algorithm; cluster.

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