Optimized control and neural observers with germinal center optimization: A review
| Autor: | Nancy Arana-Daniel, Jorge D. Rios, Javier Gomez-Avila, Carlos Lopez-Franco, Carlos Villaseñor |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Optimization algorithm business.industry Artificial immune system Computer science 020208 electrical & electronic engineering Control (management) Swarm behaviour 02 engineering and technology Multivariate optimization Identification (information) 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Artificial intelligence business Metaheuristic Software |
| Zdroj: | Annual Reviews in Control. 48:273-280 |
| ISSN: | 1367-5788 |
| DOI: | 10.1016/j.arcontrol.2019.07.001 |
| Popis: | The performance of most of nowadays control techniques depends on the choices of specific designed parameters. In the past five years, a common approach for solving the issue of finding the best designed parameters have been using metaheuristics optimization techniques. In the present review, we explore the use of the germinal center optimization algorithm (GCO) and its applications in neural identification and control. GCO is a novel artificial immune system for multivariate optimization, in contrast with other swarm optimization techniques, GCO have adaptive leadership, this enable to modify online the balance between exploration and exploitation. This is a good feature when the form of the objective function is unknown. We also explore the recent tendencies of other metaheuristics for control tuning. |
| Databáze: | OpenAIRE |
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