Desarrollo de una herramienta computacional para identificación de modelos lineales mediante optimización de parámetros utilizando colonia de hormigas y enjambre de partículas

Abstract

System identification subject is one of the most important thematic of study for automatization. It has a great amount of mathematical theory. Nonetheless, there is not a propose for system identification through bioinspired optimization algorithms like Ant Colony Optimization- ACO or Particle Swarm Optimization- PSO. Therefore, this computational tool is developed to identify and estimate the characteristic parameters for four linear invariant time model systems. Due to the metaheuristic differences between booth algorithms a third is considered. This is ACO for continuous domains named ACO-R that has booth metaheuristics. SysID is the name of the developed computational tool and it considers a previous identification and a redistribution for the metaheuristics component in contrast with the system to optimize. The graphic user interface was design according ISA 101 norm. Simulate and practical proofs had been made for proving the functionality of the tool.