Meta-heuristics applied to system identification
Meta-heuristics, Systems identification, Selection of structures, NARX model.
The systems identification have the goal to determine mathematical models to des- cribe the dynamic characteristics of systems from observations. Generally, the identi- fication process is divided into the following steps: i) experimental data collection, ii) determination of model structure, iii) parameter estimation and iv) model validation. In this work, the problem of the determination of structures is investigated. From the opti- mization techniques known as meta-heuristics, an algorithm was developed to determine the structure of polynomial NARX models. Unlike traditional methods, metaheuristics use a set of possible solutions and strategies, usually based on nature, to find the solution of the case applied. Among the techniques studied are the genetic algorithm, the particle swarm optimization, and the bat algorithm. In order to evaluate the performance of the proposed methodology, a small electric heater was identified from the data collected, later the models identified by the methods were compared with a model obtained by traditional techniques. The simulated results demonstrate that metaheuristics can be applied to the problem of the selection of structures in polynomial NARX models.