Finding fuzzy identification system parameters using a new dynamic migration period-based distributed genetic algorithm
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-Sugen...
- Autores:
-
Castro, Marco Antonio
Herrera Fernández, Francisco
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2009
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/26373
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/26373
http://bdigital.unal.edu.co/17420/
- Palabra clave:
- on-line identification
Takagi-Sugeno-Kang fuzzy model
distributed genetic algorithm
cluster.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | 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. |
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